Senior Software Engineer - Machine Learning Infrastructure

Welcome to the Journey of Joining the Daangn Team!At Daangn, we are committed to fostering an environment where individuals can grow alongside the company's success.Our recruitment team is here to assist you in achieving those joyful moments of collaboration with amazing colleagues. Introducing the ML Infrastructure TeamThe ML Infrastructure Team within our …

Who We AreAt Twelve Labs, we are on a mission to redefine the global standards for video understanding AI! We are creating world-class AI models tailored for video data, providing specialized search, analysis, summarization, and insight generation capabilities.Our models are utilized by the largest sports leagues worldwide to swiftly and accurately highlight key moments in vast game footage, delivering an ultra-personalized viewing experience. In South Korea, our technology assists integrated command centers in efficiently navigating CCTV footage to respond to crises, while major global broadcasters and studios leverage our models for content creation aimed at billions of viewers.As a Deep Tech startup with offices in San Francisco and Seoul, Twelve Labs has been recognized as one of the world's top 100 AI startups by CB Insights for four consecutive years. We have secured over $110 million in investments from leading VCs and companies including NVIDIA, NEA, Index Ventures, Databricks, and Snowflake. Our AI model, uniquely developed in Korea, is exclusively offered through Amazon Bedrock. We thrive on building innovative products alongside exceptional colleagues and growing with clients worldwide.Our core values are foundational to our work:Honesty and reflection about ourselves and our teamsPerseverance and humility in the face of failure and feedbackA commitment to continuously elevate team capabilities through ongoing learningIf you enjoy tackling challenging problems and growing through the process, the opportunity awaits you at Twelve Labs!About the TeamThe ML Data Team at Twelve Labs believes that 'data determines the performance of AI models.' We construct high-quality data end-to-end necessary for training and evaluating multimodal AI models. Our team is responsible for collecting, filtering, processing, and labeling diverse multimodal data such as video, image, and audio. We collaborate with various teams to design training data that can unleash new model capabilities, while also developing evaluation datasets that reflect real user experiences. To perform all these processes efficiently, we develop and continuously improve internal tools.Through a meticulously designed data pipeline, the ML Data Team plays a crucial role in developing Twelve Labs' world-class video understanding models.About the RoleAs the Engineering Manager for the ML Data Team, you will be a pivotal leader in building and guiding a high-performing team, while also developing the large-scale data infrastructure necessary for AI model training. This position encompasses team operations, technical architecture design, engineering recruitment and mentoring, and enhancing project execution, all aimed at fostering a high-quality data ecosystem that supports Twelve Labs' products and research.In this Role, You WillTeam Building and Technical Culture Development Lead the ML data engineering team by defining and executing the team's mission and technical strategy. Establish and execute recruitment plans to attract top talent, while fostering a high-level engineering culture through code reviews and technical mentoring.Design and Operate Multimodal ML Data Engine Design and operate pipelines for collecting, refining, and labeling petabyte-scale video, image, and audio data for AI model training. Additionally, build an automated dataset generation system for VLM/LLM training and a high-precision preprocessing engine.Design and Operate Multimodal ML Data Infrastructure Create and manage scalable data infrastructure capable of reliably processing large-scale data.Establish and Execute Data Strategy Collaborate closely with PM, research, infrastructure, product, and other organizations to establish and prioritize a data strategy aligned with the company's vision.

CLO Virtual Fashion specializes in providing comprehensive services related to clothing, from concept and design to manufacturing, marketing, fitting, and styling, all powered by advanced 3D garment simulation algorithms.Founded in 2009, CLO Virtual Fashion is at the forefront of creating a new ecosystem in digital fashion. Our flagship software includes CLO, Marvelous Designer, and Jinny, along with platforms like CLO-SET and CONNECT that are dedicated to apparel content, facilitating an efficient and sustainable workflow throughout the entire garment lifecycle.As a leader in the global digital fashion market, CLO Virtual Fashion operates 14 offices across 12 countries, including Asia, North America, Europe, and South America, continuously expanding our reach through a diverse clientele worldwide.

About the Team You Will JoinThe Machine Learning Engineer (MLOps) will be part of the ML Platform Team at Toss Bank.This team is responsible for developing and operating the machine learning platform within Toss Bank.Your ResponsibilitiesCollaborate on the development of common technologies within the ML chapter.Build and manage internal machine learning platforms using MLFlow, Airflow, Jupyterhub, and Kubeflow.Operate ScyllaDB clusters and develop Feature Store services.Develop a model serving environment based on Triton Inference Server.Establish and maintain the internal LLM platform.We Are Looking ForExperience with developing, deploying, and operating services on Kubernetes.A strong interest in machine learning and awareness of the latest trends in the field.Experience handling high-traffic applications.Experience in developing and optimizing performance using GPU-based frameworks.Experience managing distributed databases such as Apache Cassandra and ScyllaDB.Experience in building and operating LLM serving or LLMOps platforms is a plus.Strong problem-solving skills and excellent communication abilities to find optimal solutions in various situations.Resume TipsClearly describe impactful projects you've worked on.Detail your experience in building and operating machine learning platforms, including troubleshooting during operations.If applicable, share quantifiable results from improvements made to live services (omit sensitive external data).Journey to Joining Toss BankApplication Submission > Live Coding Test > Job Interview > Cultural Fit Interview > Reference Check > Compensation Discussion > Final Acceptance and OnboardingPlease NoteAny false information found in your resume or disciplinary actions in your work history may lead to cancellation of employment.Applicants who fall under the disqualification reasons per Toss Bank's employment regulations may have their applications canceled.Priority will be given to disabled individuals and national veterans in accordance with relevant laws.

Welcome to Your Journey with daangn!At daangn, we strive to foster an environment where individual growth aligns with company development.The daangn recruitment team is here to assist you in experiencing the joy of engaging with wonderful colleagues! Introducing the Search Quality TeamThe Search Quality Team focuses on revolutionizing the search experience at daangn by utilizing machine learning and natural language processing technologies to deliver personalized results. Our mission is to help users find the information they desire quickly and accurately through a contextual understanding of neighborhood life. The team is divided into three parts: Search Vertical, Search Fleamarket, and Search Place, with each ML engineer specializing in models tailored to various domains such as second-hand trading, local living, and maps.Your RoleUnderstand user search intent to recommend personalized keywords, collections, and products.Enhance search quality using natural language processing, graph-based models, and personalization algorithms.Design lightweight architectures for real-time model serving.Manage experiments and operations for model improvements in collection ranking, knowledge graphs, and keyword suggestions.We Are Looking ForExperience in designing and operating machine learning-based recommendation/search/ranking systems.Background in developing natural language processing (NLP) or graph-based recommendation systems.Experience building Python-based machine learning training pipelines.Experience in experimental design and A/B testing using large datasets.Experience in system implementation considering model serving and performance optimization.Bonus Points If You HaveModeling experience with search query auto-completion, related search terms, and typo correction.Experience serving real-time deep learning model inference.Experience leading specific domains (ranking/recommendation) with ownership in an ML engineering team.Please NoteOnboarding content and responsibilities may vary according to your capabilities.Full-time hires will undergo a 3-month probation period.According to the 'Act on the Promotion of Employment for the Disabled' and the 'Act on the Honorable Treatment and Support of Veterans', candidates with disabilities and veterans will receive preferential treatment during the recruitment process.The Application Process1. Document Screening → 2. Video Interview → 3. Job Interview → 4. Culture Fit Interview → 5. Final Acceptance → 6. JoiningGo to daangn's Joining Journey Guide ()

About the Team You Will Join The ML Engineer (OCR) will be part of the Automation Platform Team at Toss Securities. The Automation Platform Team (APT) is dedicated to solving problems through technology to provide real value, aiming to enhance Toss Securities' productivity by 10x and create sustainable scale-up opportunities. This team is responsible for developing and operating various automation products, including OCR, scraping, and QA automation, handling end-to-end engineering tasks. Your Responsibilities Upon Joining Develop OCR for Retail Operations Automation at Toss Securities. Automate various retail operations currently handled manually using OCR technology. Gradually integrate documents issued by external organizations and internal review content into the OCR pipeline, improving recognition rates for edge cases and expanding coverage. Collaborate directly with domain POs/engineers to define problems and design realistic solutions that OCR needs to address. Build the OCR Learning Pipeline. Fine-tune and enhance OCR/VLM models. Lead all stages from data collection/preprocessing/augmentation/evaluation/deployment as an ML Engineer. Manage the OCR Model Stack. Handle the current stack, including open-source OCR/VLM, document layout, and orientation classifiers, replacing models as needed or introducing in-house trained models. Take responsibility for quality across the entire process, including pre-processing and post-processing logic. Ensure Model Stability in the Operating Environment. Work closely with service engineers to enhance runtime stability and accuracy. Ideal Candidate Profile Experience in Image/Document Processing: Proficient in Python (OpenCV, PyMuPDF) and Node (sharp) for image/document processing. Experience with categorizing large volumes of images and optimizing embedding and indexing structures is a plus. VLM/OCR Modeling Experience: Demonstrated experience in applying and evaluating SOTA VLM/OCR models. Familiarity with domain-specific tuning techniques like LoRA, optimizing accuracy and availability of smaller models, and experience with document layout models is beneficial. Experience in Designing Learning Data Pipelines: Have synthesized domain documents and automatically generated labels or designed augmentation strategies that mimic actual input distributions (e.g., scan, fax, JPEG). Experience developing under conditions of limited training data is favorable. Additional Preferred Experience Understanding and handling the peculiarities of financial domain documents, including personal data tagging and low-quality scans. Ability to independently manage the entire pipeline from data collection/preprocessing to modeling and service application. Familiarity with tools like DVC and MLflow for managing training data and experiment results.

Join Our Team As a Machine Learning Engineer at Toss Securities, you will be part of the AI Tribe, collaborating with Data Engineers, Server Engineers, Frontend Engineers, Product Owners, and Product Designers. The AI Tribe aims to create data services that provide essential information to investors using data from various securities domains and cutting-edge Machine Learning technologies. Our focus is on developing personalized recommendation systems and utilizing LLM-based technologies. Your Responsibilities Develop personalized services based on diverse securities domain data and user behavior data. Experiment with reinforcement learning, deep learning, and machine learning methodologies to build recommendation models. Formulate and validate hypotheses regarding user information consumption, enhancing our services in the process. Define the criteria for recommendations and user profiles beyond simple item suggestions. Ideal Candidate Experience in validating hypotheses through personalization and recommendation systems in real-world applications. Strong foundational knowledge of recommendation system domains. Hands-on experience experimenting with and optimizing various deep learning and machine learning-based recommendation models. Familiarity with modeling based on actual user behavior data from apps. Resume Tips Detail your projects or services, including their objectives and outcomes. Focus on impactful projects or services. Explain the problems you solved and the technologies you used. If certain information is sensitive, please omit those details. Technologies Used at Toss Securities Predict user behaviors through User Modeling based on diverse user data. Build deep learning recommendation models by defining and utilizing User Features. Implement Multi-Armed Bandit (MAB) technology in our recommendation services. Generate and validate data as needed, fine-tuning and testing Large Language Models. Conduct experiments and build Retrieval/Chunking/Reranker/Generation models to establish RAG systems. Application Process Application Submission > Job Interview > Cultural Fit Interview > Reference Check > Compensation Negotiation > Final Offer and Onboarding Important Notes Any false information found in your resume or documents may lead to cancellation of your application. Individuals prohibited from hiring under Toss Securities regulations may have their applications canceled. Disabled individuals and those eligible for national veterans' benefits will be given preference in accordance with relevant laws. A Message to Future Colleagues “We are looking for colleagues who are ready to innovate financial services through Machine Learning technology!” Investors in the financial market require vast amounts of information to make informed decisions, but knowing what information to seek and where to find it can be challenging.

About the Team You Will Join The Machine Learning Engineer (LLM) at Toss Securities is part of the AI Tribe. You'll collaborate with various Data, Server, Frontend Engineers, Designers, and Product Owners to tackle product-level challenges. The goal of the AI Tribe is to simplify complex financial and securities information, delivering only the most relevant data to individuals through data and ML-based services. To achieve this, we experiment with a variety of ML techniques, including NLP/LLM training and operation, AI service development, and personalized recommendations, integrating these into real products. Your Responsibilities Upon Joining As a Machine Learning Engineer (LLM) at Toss Securities, you will take on two primary roles. If you excel in one area, you can still make significant contributions to the team. Model Training, Serving, and Operations You will train models suited for text-based challenges (e.g., classification, extraction, summarization, translation). Enhance model quality through fine-tuning, experimental design, and performance evaluation. Serve models in a live service environment and ensure their stable operation. Monitor performance degradation and changes in data distribution, implementing improvements as needed. Defining AI Service Problems, Data Construction, and AI Feature Design Reframe complex financial and securities domain issues into solvable ML/LLM problems. Define and construct datasets to facilitate model training. Generate advanced insights based on various financial data such as disclosures, news, and financial data. Collaborate with multiple teams to ensure AI features operate seamlessly within products. We Are Looking For Individuals with experience in creating and deploying LLM or NLP models. Those who have taken full responsibility for the entire cycle from data definition to model operation. Insights into utilizing LLMs effectively and reliably are a plus. Ability to logically explain why specific approaches are appropriate in problem-solving. Interest in the securities domain and simplifying complex information through technology. Preferred Qualifications Experience in developing and enhancing a single LLM/NLP model from start to finish. Experience analyzing performance degradation or data changes in service operations and implementing structural improvements. Experience improving AI features from the perspective of user experience or product metrics rather than solely model performance. Resume Recommendations Clearly state the problems you aimed to solve, the methods you chose, and the resulting changes (e.g., metrics or product modifications). Include improvements attempted during the operational phase, not just model development. Be specific about your roles within LLM, NLP, or services. Focus on the overall structure and your key contributions. Exclude sensitive information that cannot be disclosed publicly. The Journey to Joining Toss Securities Application Submission > Phone Interview > Job Interview > Cultural Fit Interview > Reference Check > Compensation Negotiation > Final Offer and Onboarding

About TwelveLabsAt TwelveLabs, we are at the forefront of developing innovative multimodal foundation models that enable video comprehension akin to human understanding. Our groundbreaking models have set new benchmarks in video-language integration, allowing for enhanced interaction and analysis of diverse media forms.With over $110 million raised in Seed and Series A funding, we are proud to be supported by esteemed venture capital firms including NVIDIA’s NVentures, NEA, Radical Ventures, and Index Ventures, alongside notable AI pioneers such as Fei-Fei Li and Silvio Savarese. Our headquarters in San Francisco, coupled with a significant presence in Seoul, signifies our dedication to global innovation.Our strategic partnerships with industry leaders like NVIDIA and AWS provide us access to advanced hardware, including B300s, enabling us to redefine the limits of video AI technology.We celebrate the individuality of our team members and believe that diverse backgrounds foster innovation. We seek passionate individuals who are driven by our mission to reshape technology and make a substantial impact. Join us in our quest to revolutionize video understanding and multimodal AI.

# Join Our Team The Machine Learning Engineer (Service) will be part of the ML Tribe within the Data Division at Toss Bank. Our team aims to tackle challenges in the financial sector that have been unaddressed, utilizing AI technologies (LLM, ML) to optimize operations and maximize profitability. Your Responsibilities Develop and operate agents aimed at optimizing financial operations. Design and develop systems for serving stable and scalable financial ML models. Ideal Candidate Profile Experience in developing and operating ML services with high SLAs in high-traffic environments is essential. Proficient in designing service architectures that balance stability and scalability, while writing maintainable, high-quality code. Experience or interest in optimizing work through agents is preferred. Familiarity with technologies such as FastAPI, LLM, Agent, Kafka, Spark, Flink, and Airflow is a plus. A track record of leveraging AI technologies to solve complex problems or develop impactful services/products is desirable. Resume Writing Tips Showcase practical development experience with concrete examples. Detail the technical challenges you have addressed and how you implemented solutions. If applicable, describe experiences comparing the cost-effectiveness of choices made between architectural complexity and development resources. Highlight projects that have generated business impact. If you have experience applying developed products in production environments and refining them based on performance metrics, please include that. Your Journey to Joining Toss Bank Application Submission > Live Coding Test > Technical Interview > Cultural Fit Interview > Reference Check > Compensation Discussion > Final Acceptance and Onboarding Please Note Employment may be rescinded if any false information is found in the resume or if there are disciplinary issues in your work history. Applicants who fall under the disqualification criteria as per Toss Bank's employment regulations may have their applications canceled. Individuals with disabilities and veterans are given preferential treatment in accordance with relevant laws.

About UsJoin us in setting global standards for AI in video understanding!At Twelve Labs, we create world-class AI models specialized in processing vast video datasets to provide advanced features such as search, analysis, summarization, and insights generation.Our models are utilized in the world's largest sports leagues to swiftly and accurately highlight key moments, enhancing the viewing experience. In domestic integrated control centers, our technology assists in efficiently navigating CCTV footage for rapid crisis response, while major broadcasters and studios globally rely on our models to produce content for billions of viewers.Twelve Labs is a Deep Tech startup with offices in San Francisco and Seoul, recognized as one of the top 100 AI startups globally by CB Insights for four consecutive years. We've secured over $110 million in investments from leading VCs and companies such as NVIDIA, NEA, Index Ventures, Databricks, and Snowflake. Our AI models are uniquely available through Amazon Bedrock, developed in Korea. We are dedicated to creating exceptional products alongside outstanding colleagues and growing with our global clientele.Our core values include:A reflective and honest attitude towards oneself and the team.Perseverance and humility in the face of failure and feedback.A commitment to continuous learning and team empowerment.If you enjoy solving challenging problems and growing through the process, the opportunity awaits you at Twelve Labs.About the TeamYou will be part of the Marengo team, responsible for the research and development of our multimodal embedding models. We integrate various modalities such as video, audio, and text into a singular embedding space.Our work encompasses a variety of research topics, including contrastive learning, temporal video understanding, and multimodal representation learning. We manage the entire model development lifecycle, from constructing large-scale training data pipelines to designing model architectures, optimizing distributed training, and developing evaluation systems. With access to top-tier GPU resources like the NVIDIA B300, we efficiently conduct large-scale experiments.In a fast-paced environment with a short research-to-production gap, we collaborate closely with the Search, Product, and Infrastructure teams to continuously enhance the quality of models used by thousands of customers worldwide.About the RoleAs a Senior ML Research Engineer on the Marengo team, you will spearhead the research and development of Twelve Labs' multimodal embedding models, focusing on data strategy, training pipeline optimization, and model architecture experimentation and evaluation.This is a research-intensive engineering role lying at the intersection of multimodal representation learning, large-scale distributed training, and data engineering. We seek a proficient engineer-researcher capable of tackling well-defined research problems with moderate ambiguity, designing rigorous experiments, and delivering reproducible results ready for production deployment.

Join Coupang as a Senior Staff Machine Learning Engineer in our Eats Search & Discovery team!In this role, you will leverage cutting-edge machine learning techniques to enhance our search capabilities and improve the customer experience. Your expertise will guide the development of innovative algorithms that power our platform.Key Responsibilities:Design and implement scalable machine learning models.Collaborate with cross-functional teams to integrate models into production.Analyze data to improve algorithms and enhance performance.Mentor junior engineers and contribute to the team’s knowledge base.

Join Our Innovative AI Team At zoyi, we have developed ALF, an AI-powered consultation agent that handles thousands of customer inquiries daily. With over 2,000 clients across diverse sectors, we have achieved a remarkable increase in ALF's solution rate, rising from 52% to 80% in 2025. However, the remaining 20% presents complex challenges. Simply applying frontier models does not ensure a positive customer experience. Each client has unique domains, and even the same questions can have different intents and contexts. Our AI team bridges this gap. We strive to ensure that our AI comprehends and utilizes client knowledge effectively, automatically measures consultation quality, creates improvement loops, and designs agents that can autonomously handle more complex inquiries. From defining AI/ML problems to proof of concept and production, we collaborate closely with feature teams. * We are hiring 2 positions (Entry to Experienced level), military service exemptions (industrial personnel, research personnel) are available. Interested in learning more about our channel team? Visit our Website Check out our Blog Watch us on YouTube Follow us on Instagram

-Legal Entity: Hyperconnect-Brand: Match Group AIAbout Match Group AIMatch Group AI is a pioneering technology organization dedicated to leveraging artificial intelligence to solve core challenges in online dating. We focus on critical areas of user experience, such as profiles, matching algorithms, and Trust & Safety, to unearth problems and innovate user experiences. By employing the latest AI technologies and data-driven methodologies, we are transforming how users interact with our platforms. Furthermore, we collaborate with global dating brands like Tinder and Hinge to enhance our shared technological foundation.To learn more about our ongoing projects, please check out this article. "Introducing the Match Group AI Team." Meet the Match Group AI ML TeamOur ML Team is composed of skilled Machine Learning Engineers who apply AI/ML technologies across Match Group’s diverse services. Our work begins by identifying and defining problems that emerge during the product development and operational phases. We develop or replicate the most suitable State-of-the-Art (SotA) models and deploy them reliably and efficiently in mobile and server environments. Through continuous monitoring and enhancements, we build the AI Flywheel for our services. This process involves close collaboration with backend, frontend, and DevOps engineers, data analysts, and project managers to create impactful AI experiences for actual users. For more insight into our working dynamics, please refer to the following resources.- [How AI Lab Works] Interview with Head of AI - Shurain- AI in Social Discovery (Blending Research and Production)Some of our work achievements are shared externally as papers or open-source code. When creating ML models for product use, existing research often falls short. To address these gaps, project participants collaboratively refine the research outcomes, and if possible, share the code. As a result, we have achieved approximately 20 notable research contributions, including the following.- 2024 CUPID: Real-time Session-based Mutual Recommendation System for 1:1 Social Discovery Platform - presented at ICDM Workshop- 2023 TiDAL: Active Learning Techniques Based on Model Behavior for Efficient Learning Processes - published at ICCV 2023- 2023 ...

# About the Team You'll Join- As a Machine Learning Engineer at Toss, you will be part of diverse business units and teams, playing a crucial role in quickly solving and automating various challenges through machine learning.- You will employ ML technologies to address issues across multiple business domains and provide standardized technical products to the Toss teams.- Candidates will be assigned to domains and tasks based on their strengths and experiences during the interview process.- **Want to learn more about Toss's Data Organization?** [→ *Toss Data Division Wiki*](https://recruit-data-division.oopy.io/)# Responsibilities You Will Take On- (Commerce) Design and enhance recommendation algorithms to optimize product visibility in the commerce domain and develop models to predict metrics like click-through rates (CTR) and conversion rates (CVR).- (MLOps) Build and provide a platform and Kubernetes infrastructure that allows for safe and rapid experimentation and deployment of various ML requirements.- (Search) Design and operate a search platform focused on real-time processing of large data volumes, managing search infrastructure, and improving search quality to enhance customer experience.- (AI) Leverage various AI technologies such as LLM, RAG, and multimodal approaches to define problems and design technical solutions.# Ideal Candidate Profile- We are looking for individuals with a deep understanding of machine learning and experience applying it to various business problems.- Familiarity with big data platforms such as SQL, Hadoop, and Spark is advantageous.- Experience in designing and developing models for actual service deployment, along with analyzing the outcomes, is highly desirable.- Practical experience with major ML libraries such as PyTorch, TensorFlow, XGBoost, and LightGBM is a plus.- If you have a track record of quickly immersing yourself in new challenges and learning necessary skills to solve them independently, that would be even better.# Resume Recommendations- If you have developed machine learning algorithms and applied them in operations, please detail the technologies used and the resulting changes.- Highlight both team achievements as well as your individual contributions.- Even if your experience is limited, if your problem definition and resolution process is well articulated, it can create a positive impression.# Important Notes- Industrial functional personnel can be newly incorporated into supplementary tasks or transferred.- Professional research personnel can also be newly incorporated or transferred from active duty.# Joining Toss Process- Application submission > 1st Round Technical Interview (Coding) > 2nd Round Technical Interview > Cultural Fit Interview > Final Acceptance# A Message for Future Colleagues- “This role goes beyond simple modeling; it has a real impact on the business.”- The most satisfying aspect of working at Toss is that we do more than just modeling.- Previously, the role was limited to inputting data into existing models and evaluating performance, but now we focus on how to integrate unaggregated data into our models.- Moving beyond just analyzing financial data, I find it rewarding to contribute to the operations of a super app, leveraging our understanding of users!

About Coupang Coupang is dedicated to delivering exceptional customer experiences. We strive to amaze our customers, and when they say, “How did we ever live without Coupang?”, we know we are achieving our mission. Founded with a passion for simplifying shopping, dining, and living, we are transforming the multi-billion-dollar commerce landscape in South Korea, establishing a reputation as a trusted leader in the industry. We blend the agility of a startup with the robust resources of a large, publicly traded company. This unique combination empowers us to innovate and expand our services rapidly, maintaining the momentum we've had since our inception. Our team is filled with entrepreneurial individuals eager to take initiative and drive impactful innovations. At Coupang, you will witness personal and professional growth, both for yourself and your team. Join us in our mission to redefine commerce. We challenge conventional limits to solve problems and create unparalleled customer experiences in an increasingly digital and interconnected world.

About Match Group AIMatch Group AI is a pivotal technology organization dedicated to utilizing AI to tackle key challenges in online dating. We concentrate on enhancing user experience in areas such as profiles, matching, and Trust & Safety by identifying challenges and innovatively addressing them through cutting-edge AI technologies and data-driven approaches. Our collaboration with global dating brands like Tinder and Hinge enables us to expand our common technological foundation across the Match Group.Introducing the Match Group AI ML TeamOur ML Team, composed of skilled Machine Learning Engineers, applies AI and ML technologies to various Match Group services. The team's mission begins with identifying and defining issues that arise during the development and operation of actual products. We develop or reproduce the most suitable State-of-the-Art (SotA) models, ensuring their stable and efficient deployment in mobile and server environments. Continuous monitoring and improvement are integral as we build the AI Flywheel for our services. Collaboration with diverse specialties, including backend/frontend/DevOps engineers, data analysts, and PMs, is fundamental to creating impactful AI experiences for our users.To learn more about our projects and working environment, feel free to check out the following: "Introducing the Match Group AI Team."

About CoupangAt Coupang, our mission is to amaze our customers. When we hear them exclaim, “How did we ever live without Coupang?” we know we’re on the right track. Born from a desire to simplify shopping, dining, and daily living, we are revolutionizing the multi-billion-dollar commerce sector in South Korea and establishing a reputation as a trusted leader in the industry.We enjoy the best of both worlds — a dynamic startup environment backed by the resources of a large, publicly traded company. This enables us to maintain rapid growth and launch innovative services at an unprecedented pace. Our culture fosters entrepreneurship and offers abundant opportunities for driving new initiatives and innovations. At Coupang, you will witness continuous personal and professional growth for yourself, your colleagues, your team, and the company.Our commitment to shaping the future of commerce is genuine. We challenge the limits of possibility to tackle challenges and redefine conventional trade-offs. Join Coupang today to craft extraordinary experiences in an always-connected, high-tech world. Team OverviewThe Search and Recommendation team enhances the product discovery experience for Coupang customers, focusing on search and recommendation quality, product ranking on category pages, and review ranking.This area is rapidly evolving, and we are dedicated to improving the search and recommendation quality to ensure customers discover their ideal products effortlessly. Our aim is to deliver a ‘wow’ shopping experience by presenting customers with products they love, even before they express their intent—this is one of the finest discovery experiences in e-commerce. We leverage cutting-edge Machine Learning and Deep Learning technologies to guarantee top-notch recommendations, continuously innovating and building highly scalable systems to support our growing business and customer engagement. Role OverviewAs a Senior Staff Machine Learning Engineer specializing in Search and Recommendations, you will be pivotal in designing, developing, maintaining, and enhancing the end-to-end search and recommendation systems. Your responsibilities will include overseeing our online search and recommendation ranking models, managing multiple data pipelines that produce candidates and features (both offline and online), and maintaining a serving system that generates search and recommendation results, along with product ranking outcomes across Coupang and Coupang Eats.

CLO Virtual Fashion is a pioneering company specializing in 3D garment simulation algorithms, offering comprehensive services from concept and design to manufacturing, marketing, fitting, and styling.Founded in 2009, CLO Virtual Fashion is at the forefront of creating a new ecosystem in digital fashion.With our clothing design software such as CLO, Marvelous Designer, and Jinny, along with platforms like CLO-SET and CONNECT, we engage in every aspect of garment creation to establish efficient and sustainable workflows. Our solutions allow for seamless interaction between real and digital clothing, functioning within the CLO system.As a leader in the global digital fashion market, CLO Virtual Fashion operates 14 offices across 12 countries including Asia, North America, Europe, and South America, continually expanding based on a vast customer base.

About UsAt TwelveLabs, we are at the forefront of creating innovative multimodal foundation models that empower machines to understand videos as humans do. Our groundbreaking models have set new benchmarks in video-language modeling, enhancing our analytical capabilities and revolutionizing media interaction.With over $110 million raised in Seed and Series A funding, we are supported by leading venture capital firms, including NVIDIA’s NVentures, NEA, Radical Ventures, and Index Ventures, alongside esteemed AI pioneers like Fei-Fei Li and Silvio Savarese. Headquartered in San Francisco, we also boast a significant presence in Seoul, reflecting our dedication to global innovation.Our strategic alliances with NVIDIA and AWS provide us access to state-of-the-art technology, including the B300 chips, enabling us to push the limits of video AI capabilities.We celebrate diversity and believe that the unique journeys of each individual contribute to our innovative culture. We are in search of passionate individuals who resonate with our mission and are eager to drive technological transformation. Join us in reshaping the future of video understanding and multimodal AI.Team OverviewThe Pegasus team is central to TwelveLabs' video comprehension capabilities, spearheading our Video Analysis product. We focus on creating advanced multimodal video analysis systems that excel in instruction adherence and yield complex, hierarchically structured outputs. Our mission is to deliver products with tangible real-world applications, working collaboratively across functional teams of ML researchers and engineers.Our initiatives encompass various challenges, including large-scale distributed training of multimodal LLMs, precise temporal segmentation, and robust metadata extraction for practical applications, extending temporal context to several hours, alongside data curation processes that enhance evaluation and performance through improved training data.Our team leverages cutting-edge hardware, such as NVIDIA B300s, to accelerate the transition from research to production, ensuring swift and effective deployment of our innovations.