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Experience Level
Experience
Qualifications
Qualifications:Proven hands-on experience in training and fine-tuning large language models (LLMs) and vision-language models (VLMs), including practical involvement in pre-training, instruction tuning, and alignment techniques (GRPO, RLHF/DPO/PPO). Strong background in deep learning models, particularly focusing on Transformers. Capability to convert innovative research findings from academic papers into clean, production-ready code (Paper to Code). Demonstrated experience in scaling inference infrastructure for LLMs/VLMs, including proficiency in model serving frameworks such as vLLM and TGI. Proficient in Python with a solid track record of significant project development. A strong foundation in computer science fundamentals (data structures, algorithms, design patterns). Bachelor's degree in Computer Science or a related technical field. Extensive experience with ML frameworks and libraries (PyTorch, TensorFlow). Self-motivated individual comfortable thriving in a fast-paced, dynamic work environment.
About the job
Join Nace. AI as a Machine Learning Engineer, where you will be instrumental in transforming advanced machine learning research into scalable, production-ready applications. Collaborating with interdisciplinary teams, you will pinpoint areas where machine learning can enhance product offerings, design robust model-centric architectures, and guarantee their smooth integration into practical applications. This role demands a harmonious blend of theoretical insight and hands-on engineering, focusing on creating dependable, maintainable, and impactful AI-driven features that align with Nace. AI's strategic goals.
Key Responsibilities:
Develop and sustain complete ML systems, including synthetic data pipelines, model training, debugging, and performance assessment.
Enhance large language models (LLMs) and utilize meta-learning strategies to boost model generalization and efficiency.
Refine existing Nace. AI models by integrating breakthroughs from the latest ML research.
About Nace.AI
Nace. AI is at the forefront of AI innovation, dedicated to leveraging machine learning techniques to enhance user experience and drive value across various applications. We foster a collaborative environment where creativity meets technology, ensuring our products are not just functional but also transformative.
About VoltaiAt Voltai, we are pioneering the future of artificial intelligence by developing world models and agents capable of learning, evaluating, planning, experimenting, and interacting with the physical world. Our initial focus is on understanding and creating advanced hardware, electronic systems, and semiconductors, utilizing AI to design and innovat…
At Rhoda AI, we are pioneering the future of humanoid robotics by establishing a comprehensive stack that includes advanced, software-defined hardware along with foundational models and video world models to drive our innovations. Our robots are engineered to be versatile, capable of navigating complex real-world scenarios that extend beyond traditional training environments. Our interdisciplinary research team, featuring experts from prestigious institutions such as Stanford, Berkeley, and Harvard, is at the forefront of large-scale learning, robotics, and systems engineering. With over $400 million raised, we are making significant investments in research and development, hardware innovation, and scaling our manufacturing capabilities to bring our vision to life.We are seeking a motivated Machine Learning Inference Engineer to join our team and contribute to the development and operation of the inference systems that power our automation stack. You will play a crucial role in ensuring the efficient and reliable execution of large foundation models, collaborating closely with our robotic platforms and internal task tools.Key Responsibilities:Develop and maintain infrastructure for model inference across both cloud and on-premises environments.Optimize the latency, throughput, and reliability of deployed machine learning models.Design and scale services for serving diverse foundation models in both research and production contexts.Collaborate with research and robotics teams to enhance inference optimization and integration.Create tools for model deployment, version control, and observability to facilitate rapid iteration cycles.Contribute to the robustness and scalability of the inference stack as model complexity and deployment demands evolve.Qualifications:Minimum of 3 years of experience in machine learning infrastructure, MLOps, or backend systems.Proven experience in deploying and managing machine learning inference workloads in production environments.Excellent knowledge of Kubernetes and containerized deployment pipelines.Familiarity with cloud service providers such as AWS and GCP, including GPU orchestration capabilities.Experience with popular ML frameworks including PyTorch and TensorFlow, as well as model serving tools like Triton, TorchServe, and Ray Serve.Strong debugging capabilities and a proactive ownership mindset, comfortable resolving issues across the technology stack.
About Mistral AIAt Mistral AI, we harness the transformative power of artificial intelligence to streamline tasks, save valuable time, and foster enhanced creativity and learning. Our innovative technology is crafted to effortlessly integrate into everyday work environments.We are committed to democratizing AI by offering high-performance, optimized, open-source models, products, and solutions. Our extensive AI platform caters to both enterprise and individual needs, featuring products like Le Chat, La Plateforme, Mistral Code, and Mistral Compute—creating cutting-edge intelligence accessible to all users.As a vibrant and collaborative team, we are driven by our passion for AI and its potential to revolutionize society. Our diverse workforce excels in competitive settings and is dedicated to fostering innovation. With teams distributed across France, the USA, the UK, Germany, and Singapore, we pride ourselves on our creativity, humility, and team spirit.Join us in shaping the future of AI at a pioneering company. Together, we can create a lasting impact. Discover more about our culture at https://mistral.ai/careers.Role OverviewAbout the Research Engineering TeamThe Research Engineering team operates across Platform (shared infrastructure & clean coding practices) and Embedded (integrated within research squads). Our engineers have the flexibility to navigate the research↔production spectrum as their interests and needs evolve.As a Machine Learning Research Engineer, you will be responsible for building and optimizing large-scale learning systems that underpin our open-weight models. Collaborating closely with Research Scientists, you may join either:- Platform RE Team: Focus on enhancing our shared training frameworks, data pipelines, and tools utilized across all teams; or- Embedded RE Team: Become part of a research squad (Alignment, Pre-training, Multimodal, etc.) to turn innovative ideas into scalable, repeatable code.Key Responsibilities• Support researchers by managing the complex aspects of large-scale ML pipelines and developing robust tools.• Bridge cutting-edge research with production: integrate checkpoints, optimize evaluations, and create accessible APIs.• Conduct experiments utilizing the latest deep-learning techniques (sparsification on 70B+ models, distributed training across thousands of GPUs).• Design, implement, and benchmark ML algorithms; produce clear and efficient code in Python.• Deliver prototypes that evolve into production-grade components for Le Chat and our enterprise API.
Join our team at Mind Robotics as a Machine Learning Infrastructure Engineer, where you'll play a pivotal role in developing the systems that facilitate effective large-scale model training. This position is ideal for individuals who thrive in high-scale environments—overseeing distributed training, managing core ML infrastructure, and leveraging rapid iteration loops across hundreds of GPUs. If you have experience building or managing large training systems in frameworks like PyTorch or JAX and have a passion for optimizing processes such as sharding, parallelism, and performance, you'll find a welcoming environment here. Collaborate closely with researchers to minimize friction, enhance reliability, and streamline the processes for training, evaluating, and deploying models that integrate into real-world applications.
Full-time|$211.3K/yr - $385K/yr|On-site|Austin, Texas, United States; Chicago; Palo Alto, California, United States
Upwork Inc. connects businesses with skilled professionals in AI, machine learning, software development, sales, marketing, customer support, finance, and accounting. The company’s platforms, including the Upwork Marketplace and Lifted, help organizations of all sizes find and manage freelance, fractional, and payrolled talent for a range of contingent work needs. Upwork supports both large enterprises and entrepreneurs in sourcing talent and implementing AI-driven solutions. The company’s network covers more than 10,000 skills, enabling clients to scale and adapt their workforce for changing business demands. Since launch, Upwork has processed over $30 billion in transactions. The company’s mission centers on expanding opportunities at every stage of work. Learn more Visit the Upwork Marketplace: upwork.com Learn about Lifted: go-lifted.com Connect on LinkedIn, Facebook, Instagram, TikTok, and X Follow Lifted on LinkedIn
Join us at Grindr as a Staff Machine Learning Engineer in a dynamic hybrid work environment, primarily based in our Palo Alto office. You will be required to work in the office on Tuesdays and Thursdays.Why This Role is Exciting:As a pivotal member of Grindr, you will play a crucial role in our AI-driven transformation. This is your opportunity to leverage advanced machine learning techniques to enhance the way millions in the LGBTQ+ community connect, whether for casual chats, fleeting encounters, or enduring relationships. We are committed to making machine learning a cornerstone of Grindr, and your contributions will leave a lasting impact on our unique global platform.Impact from Day One: Join a focused team at the forefront of machine learning initiatives, where you will engage in significant, innovative projects that lay the groundwork for our long-term ML vision.Transformative Recommendations: Develop systems that connect users to their next meaningful experiences, adapting to a variety of needs and preferences.Insightful Conversations: Utilize Large Language Models (LLMs) to extract insights, enhancing user interactions with precision and creativity.Your Responsibilities:Design and implement scalable recommendation systems to serve millions, ensuring a balance between performance and innovation.Employ cutting-edge LLMs to analyze extensive conversational data and improve user connections.Prototype, refine, and deploy production-ready ML solutions that address real user challenges.Work collaboratively with engineering, data science, and product teams to bring bold ideas to fruition.Explore and implement new AI tools and techniques to keep Grindr’s technology at the forefront.Your Qualifications:A minimum of 7 years of experience in building machine learning systems, particularly in developing systems from the ground up. Experience with recommendation systems is advantageous.Demonstrated ability to deliver scalable solutions, with proficiency in Python and popular machine learning frameworks.A proactive approach to tackling complex challenges with tangible outcomes.Familiarity with data and deployment technologies (e.g., Snowflake, etc.) is beneficial.
Join us at Grindr in a hybrid position based in our Palo Alto or San Francisco offices, with in-office attendance required on Tuesdays and Thursdays.Why This Role is Exciting:As a pivotal figure at Grindr, you will lead our transformative AI journey. This is your opportunity to leverage state-of-the-art machine learning techniques to revolutionize the way millions within the LGBTQ+ community connect, whether through engaging conversations, casual meetups, or meaningful relationships. Our commitment to machine learning is strong, and you will play an essential role in shaping our strategy and execution on this unique global platform.Impact from Day One: You will be instrumental in establishing foundational systems in an early-stage ML environment, charting the roadmap for our long-term strategy.Innovative Recommendations: Design and scale recommendation platforms that connect millions to their next significant experience, tailored to diverse user intents.Conversational Insights: Employ large language models (LLMs) to extract insights and establish best practices for conversational AI, enhancing user engagement with precision.Key Responsibilities:Develop and manage large-scale recommendation systems to serve millions of users while balancing performance and innovation.Utilize advanced LLMs to analyze extensive conversation data, enhancing connections among users.Prototype, iterate, and deploy production-ready ML solutions addressing real user challenges.Provide technical guidance across teams, collaborating with engineering, data science, and product teams to turn innovative ideas into reality.Assess and incorporate emerging AI tools and techniques organization-wide to maintain a leading-edge technology stack.Qualifications We Seek:Over 10 years of experience in building ML systems, particularly in developing 0-to-1 systems, platform architecture, and pioneering new capabilities. Familiarity with recommendation systems is advantageous.Proven track record of delivering scalable solutions, with proficiency in Python and popular ML frameworks.A proactive mindset and the ability to work in a fast-paced, dynamic environment.
At Protegrity, we are at the forefront of data protection innovation, harnessing the power of AI and quantum-resistant cryptography. Our mission is to transform how sensitive data is safeguarded across cloud-native, hybrid, and on-premises environments. Utilizing cutting-edge cryptographic techniques, including tokenization and format-preserving encryption, we ensure that data remains both valuable and secure.Join us in a collaborative environment where your contributions will directly impact our industry. By working with some of the brightest minds, you will help redefine data security in a GenAI era, where data is the ultimate currency. If you're passionate about shaping the future of data protection, then Protegrity is the place for you!
Full-time|$140K/yr - $265K/yr|On-site|San Francisco Bay Area
About Glean:Established in 2019, Glean is a pioneering AI-driven knowledge management platform that empowers organizations to swiftly locate, organize, and disseminate information within their teams. By integrating flawlessly with platforms such as Google Drive, Slack, and Microsoft Teams, Glean ensures that employees have timely access to essential knowledge, enhancing productivity and fostering collaboration. Our state-of-the-art AI technology simplifies the discovery of knowledge, enabling teams to utilize their collective intelligence more rapidly and effectively.The vision for Glean originated from the profound insights of our Founder & CEO, Arvind Jain, who recognized the hurdles employees encounter in accessing and comprehending information at work. Witnessing the fragmentation of knowledge and the overwhelming number of SaaS tools that hindered productivity, he set out to create a superior solution—an AI-powered enterprise search platform that facilitates quick and intuitive information retrieval. Since its inception, Glean has transformed into the premier Work AI platform, blending enterprise-grade search capabilities with an AI assistant and robust application- and agent-building functionalities to fundamentally reshape how employees operate.About the Role:We are on the lookout for talented engineers to join our mission of building the world's leading search and assistant product for workplace efficiency. Our engineering team engages with various systems across the technology stack, focusing on areas such as query comprehension, document analysis, domain-specific language modeling, natural language question-answering, evaluation, and experimentation. We maintain regular interactions with our customers to deeply understand their challenges and utilize the most effective tools—whether simple or complex—to address their needs.Your Responsibilities:Develop innovative signals to enhance the personalization of our search engineTrain models to analyze interactions between signals in our ranking processes
Full-time|$200K/yr - $300K/yr|On-site|San Francisco Bay Area
About Glean:Established in 2019, Glean is a pioneering AI-driven knowledge management platform designed to empower organizations to swiftly locate, structure, and disseminate information among their teams. By seamlessly integrating with tools such as Google Drive, Slack, and Microsoft Teams, Glean ensures that employees can access the right knowledge at the right time, enhancing productivity and collaboration. Our state-of-the-art AI technology simplifies knowledge discovery, making it more efficient for teams to harness their collective intelligence.Glean was founded by Arvind Jain, who recognized the challenges employees face in navigating fragmented knowledge and diverse SaaS tools that hinder productivity. With a vision to create a superior solution, he developed an AI-powered enterprise search platform that facilitates quick and intuitive access to essential information. Since then, Glean has transformed into a leading Work AI platform, integrating enterprise-grade search, an AI assistant, and robust application and agent-building capabilities, fundamentally changing the way employees work.About the Role:We are on the lookout for talented Machine Learning Engineers who are eager to engage in both Quality Assurance and traditional ML tasks to aid in the development of our revolutionary Enterprise Brain. The Enterprise Brain team is crafting a suite of proactive AI products aimed at transforming enterprise workflows by identifying and automating tasks for users, thereby unlocking genuine productivity. This initiative is based on a profound understanding of user needs and a sophisticated Enterprise graph. The role will involve leveraging both LLM and advanced ML techniques, orchestrating agents, and employing cutting-edge ranking methods.Your Responsibilities:Tackle challenging ML problems that involve...
Our VisionAt Tinder, we believe that the thrill of meeting new people is one of life’s greatest joys. We are dedicated to nurturing the magic of human connection, engaging tens of millions of users worldwide. With hundreds of millions of downloads, over 2 billion swipes daily, 20 million matches each day, and a presence in more than 190 countries, our influence is vast and continually expanding.Our team collaborates to tackle intricate challenges, blending insights from human relationships, behavioral science, network economics, AI, and machine learning, while prioritizing user safety and cultural sensitivity. We explore the depths of loneliness, love, and connection.Internship DurationThe internship will take place from June 1 to August 28, 2026.Work EnvironmentThis is a hybrid position, requiring in-office collaboration three days a week in our Palo Alto, California office.Role OverviewAs a member of the Tinder ML team, you will play a crucial role in shaping the product experience across diverse domains, including Recommendations, Trust & Safety, Profile Management, Chat, Growth, and Revenue. Our goal is to leverage machine learning to enhance user experiences, build trust, and drive business growth within Tinder's ecosystem. This internship offers a unique opportunity to work alongside experienced engineers to develop and implement machine learning solutions that align with Tinder’s strategic objectives.
At Rhoda AI, we are pioneering the development of a comprehensive full-stack platform for the next generation of humanoid robots. Our innovative approach encompasses high-performance, software-defined hardware along with foundational and video world models that empower our robotic systems. Our robots are engineered as versatile generalists, adept at navigating intricate, real-world scenarios, including those not encountered during training. Collaborating with a distinguished research team from Stanford, Berkeley, Harvard, and other leading institutions, we operate at the forefront of large-scale learning, robotics, and systems engineering. With over $400M in funding, we are aggressively investing in research and development, hardware innovation, and scaling up manufacturing to bring our vision to life.We are on the lookout for a Staff / Principal Machine Learning Engineer to take charge of our training platform. This pivotal system is essential for ensuring that large-scale training is reliable, reproducible, and straightforward to execute. You will play a crucial role in defining the lifecycle of training jobs, including their launch, tracking, recovery, and debugging across our clusters. Your contributions will enable researchers to innovate rapidly without infrastructure hindrances.In this role, you will be at the heart of enhancing research efficiency: when a training job fails, your system will allow for automatic recovery; when experiments become challenging to reproduce, you will implement effective solutions; and when GPU hours are squandered, you will ensure visibility and preventative measures are in place.
About ALSOAt ALSO, we are pioneers in electric mobility, founded as part of Rivian. Our dedicated team comprises builders, innovators, and visionaries who are passionate about developing unique, vertically integrated electric vehicles (EVs) that tackle today’s and tomorrow’s mobility challenges. Our goal is to inspire our community to choose ALSO, replacing traditional vehicle miles with more affordable, enjoyable, and efficient alternatives, achieving 10-50 times greater efficiency.The RoleWe are in search of a senior Software Engineer with a strong focus on algorithms, machine learning, and edge AI. In this position, you will decipher ambiguous customer and product requirements, model complex technical problems, and transform solutions into robust, production-ready code that operates seamlessly across mobile, embedded, and cloud platforms.Collaboration is key in this role; you will work closely with Product, Design, Embedded, Mobile, Cloud, Data, Manufacturing, and Systems Engineering teams to make informed architectural decisions regarding algorithm deployment, performance measurement, and continuous improvement.This role is well-suited for an individual who can navigate the entire process from problem definition to modeling, experimentation, implementation, deployment, telemetry, and iteration. You should adeptly balance algorithmic excellence with real-world constraints such as latency, battery life, compute power, bandwidth, reliability, and manufacturability.What You Will DoDesign, develop, deploy, and refine algorithms and machine learning models across mobile, embedded, and cloud environments.Collaborate with Product Managers, Designers, and Engineering stakeholders to convert customer needs into precise algorithmic requirements.Engage with platform and domain experts to effectively deploy algorithms in production and monitor their performance in real-world settings.Conduct performance analysis of models and algorithms through experimentation, A/B testing, shadow testing, telemetry analysis, and offline evaluation.Create and manage input/output data schemas to facilitate efficient edge-to-cloud telemetry, diagnostics, model retraining, and ongoing enhancements.Optimize deployed algorithms for factors such as latency, battery consumption, communication efficiency, robustness, and intended product functionality.Investigate anomalies, failure modes, regressions, and edge cases to enhance algorithm reliability.
Woven by Toyota is at the forefront of Toyota’s transformative journey into a mobility-focused company. With a rich legacy of innovation that prioritizes societal benefit, our aim is to redefine mobility through human-centered solutions, expanding the definition of mobility and its societal contributions.Our initiatives revolve around four core areas: Advanced Driver Assistance Systems (AD/ADAS), the Arene software development platform for software-defined vehicles, Woven City as a testing ground for mobility solutions, and our digital infrastructure encompassing Cloud & AI. Together, we strive toward a singular ambitious goal: a world devoid of accidents and enhanced well-being for everyone.TEAMAt Woven by Toyota, we are pioneers in crafting advanced Machine Learning solutions aimed at autonomous driving. Our team is dedicated to overcoming innovative challenges by designing sophisticated neural networks, developing pioneering end-to-end architectures, and enhancing machine learning techniques in perception, prediction, and motion planning. We are seeking enthusiastic innovators and skilled problem-solvers who are eager to transform mobility through cutting-edge AI and robotics, directly influencing the future of autonomous vehicle technology.Woven by Toyota is collaborating with the Toyota Research Institute (TRI) on a project focused on developing a visual-based world model as a learned simulator to assess end-to-end automated driving. This collaborative effort aligns with TRI's advanced development initiatives in Diffusion Policy and Large Behavior Models (LBM).WHO ARE WE LOOKING FOR?We are in search of a Senior Machine Learning Engineer to help construct a vision-based world model for interactive driving scenarios. The engineer will play a crucial role in linking research initiatives with production programs. This position demands excellent communication abilities and a collaborative approach to navigate the joint nature of the Woven / TRI partnership. Candidates should possess extensive technical knowledge of state-of-the-art methodologies in robotics and automated driving to guide vision and scope, initiating long-term open-research projects.
Join Our Innovative Team Nubank is a leading digital financial platform, serving over 122 million customers across Brazil, Mexico, and Colombia. Our mission is to simplify financial services and empower individuals, marking the start of a vibrant future in Latin America. As a publicly listed company on the New York Stock Exchange (NYSE: NU), we leverage cutting-edge technology and data intelligence to create financial products that are not only accessible but also user-friendly. Our achievements have earned us recognition from prestigious rankings, such as Time 100 Companies, Fast Company’s Most Innovative Companies, and Forbes World’s Best Bank. Explore more about us on our institutional page here. About the Role At AI Core, we are expanding our AI initiatives to become the backbone of Nubank's key decision-making systems. We are in search of talented Machine Learning Engineers to spearhead impactful research projects that connect advanced AI technologies with real-world financial systems. Your role will involve tackling intricate challenges using Deep Learning and Foundation Models, ensuring our solutions are scalable, efficient, and yield tangible business outcomes. As a Machine Learning Engineer (MLE), your responsibilities will include: Leading and executing complex applied research initiatives independently, focusing on building and optimizing architectures (e.g., Transformers, GNNs) for critical applications such as Credit, Recommendation Systems, Generative AI, and real-time inference. Resolving challenging and ambiguous modeling problems that necessitate collaboration across various teams (Data, Infrastructure, Product), delivering innovative solutions with a clear emphasis on medium-term impact. Connecting the research and production worlds by designing architectures that comply with MLOps constraints, ensuring models are optimized for latency, interpretability, and cost-effectiveness. We invite you to be part of our journey to revolutionize the financial landscape.
Internship|On-site|Palo Alto, California, United States
About PathwayPathway is revolutionizing artificial intelligence with the introduction of the world’s first post-transformer model that mimics human thought processes. Our innovative architecture surpasses traditional Transformer models, providing enterprises with unparalleled transparency into model operations. By integrating this foundational model with the fastest data processing engine available, Pathway empowers organizations to transcend mere incremental optimization and achieve genuinely contextualized, experience-driven intelligence. Trusted by prestigious clients including NATO, La Poste, and Formula 1 racing teams, we are at the forefront of AI advancements.Led by visionary CEO Zuzanna Stamirowska, a complexity scientist, our team includes AI trailblazers such as CTO Jan Chorowski, who pioneered the application of Attention in speech and collaborated with Nobel laureate Geoff Hinton at Google Brain, and CSO Adrian Kosowski, a distinguished computer scientist and quantum physicist who earned his PhD at just 20 years old.Supported by prominent investors and advisors like Lukasz Kaiser, co-author of the Transformer architecture (the “T” in ChatGPT) and a key researcher in OpenAI's reasoning models, Pathway is headquartered in Palo Alto, California.The OpportunityWe are on the lookout for passionate Machine Learning/AI Software Engineering interns with a solid foundation in machine learning model research.Your ResponsibilitiesAssist in training Large Language Models (LLMs)Conduct benchmarking of LLMsPrepare and evaluate training datasetsCollaborate with the core Pathway Research TeamYour contributions will significantly impact the advancement of the AI landscape.
Woven by Toyota is at the forefront of Toyota’s groundbreaking transition into a mobility enterprise. With a commitment to innovating for the greater good, our mission is to revolutionize mobility through human-centric advancements that redefine its meaning and societal impact.Our initiatives are built upon four foundational pillars: AD/ADAS, encompassing our autonomous driving and advanced driver assistance technologies; Arene, our software development platform tailored for software-defined vehicles; Woven City, a pioneering testbed for mobility innovations; and Cloud & AI, the digital backbone that supports our collaborative framework. Our business-critical functions empower these teams to collaborate effectively in pursuit of a singular, ambitious goal: to create a world devoid of accidents and enhance the well-being of all.Team OverviewThe Behavior team at Woven by Toyota addresses autonomous challenges related to prediction and trajectory planning. Our work encompasses an array of tasks, including the analysis of petabytes of multimodal driving data, optimization problem-solving, latency reduction on hardware accelerators, deployment of scalable and efficient machine learning (ML) training and evaluation pathways, and the design of innovative neural network architectures to push the boundaries of ML in Prediction and Motion Planning.Candidate ProfileWe are searching for a highly experienced, technically adept Engineering Manager to spearhead a team of talented engineers within the behavior group. This role emphasizes the development and implementation of cutting-edge machine learning models for planning and prediction in AD/ADAS, alongside their related data processing frameworks and vehicle integration. The ideal candidate should possess a profound enthusiasm for self-driving technology and its transformative potential on a global scale. You will guide your team in enhancing our core behavior machine learning models and associated pipelines. We seek an individual with deep expertise in machine learning, a thorough comprehension of the model lifecycle, and substantial experience in creating tools and pipelines to address behavioral challenges. Success in this role necessitates a candidate who flourishes in a dynamic environment, is hands-on, and is driven to expand the frontiers of our technology. Prior experience in developing and deploying real-world autonomous driving products and collaborating with exceptionally skilled engineers is crucial.
Join our innovative team at latitude as a Senior Software Engineer specializing in Machine Learning Offboarding Models. In this role, you will leverage your expertise in software engineering and machine learning to develop robust models that enhance our offboarding processes. Your contributions will directly impact our operational efficiency and improve user experiences.As a key member of our team, you will collaborate with cross-functional teams to design, implement, and optimize machine learning solutions. We seek a passionate engineer who thrives in a fast-paced environment and is eager to tackle challenging problems.
At Rhoda AI, we are pioneering the development of a comprehensive foundation for the next generation of humanoid robots. Our focus spans high-performance, software-defined hardware to advanced foundational models and video world models that govern robot functionality. Our robots are engineered to be versatile, capable of navigating intricate, real-world environments and tackling scenarios not previously encountered in training. We stand at the crossroads of large-scale learning, robotics, and systems, bolstered by a research team comprising experts from prestigious institutions such as Stanford, Berkeley, and Harvard. Our ambition is not merely to add features; we are crafting a revolutionary computing platform for physical tasks, underpinned by over $400 million in funding, driving aggressive investments in research & development, hardware innovation, and scaling up manufacturing to bring our vision to fruition.Role OverviewWe are in search of a Principal Machine Learning Systems Engineer to take charge of our training systems' performance from start to finish. You will be instrumental in defining the scaling of our model training, enhancing efficiency, scalability, and accuracy across extensive multimodal training environments. This is a pivotal systems role, not merely focused on infrastructure support. Your contributions will significantly influence our compute utilization efficiency, scalability of models across thousands of GPUs, and the speed of research iterations.Your ResponsibilitiesOversee training performance from start to finishAnalyze and enhance the performance of large-scale multimodal training encompassing vision, video, proprioception, actions, and language.Create systematic performance attributions by breaking down step-time into compute, communication, and input pipeline, along with scaling curves for various cluster sizes and identifying key bottlenecks.Drive quantifiable improvements across:Distributed efficiency (e.g., communication and compute overlap, bucketization, topology-aware mapping, and parallelism strategies).Compute efficiency (e.g., identifying kernel hotspots, operator fusion, attention optimization, and minimizing framework/runtime overhead).Memory efficiency (e.g., activation checkpointing, sequence packing, and reducing fragmentation).Design training systems rather than just tuning themDefine and refine parallelism strategies including data, tensor, pipeline, sharding, and hybrid approaches.Enhance execution efficiency through communication scheduling, graph capture, execution optimization, and runtime enhancements.Contribute to the overall system architecture with innovative solutions.
Role Overview:Join Nace.AI as a Machine Learning Engineer, where you will be instrumental in transforming advanced machine learning research into scalable, production-ready applications. Collaborating with interdisciplinary teams, you will pinpoint areas where machine learning can enhance product offerings, design robust model-centric architectures, and guarantee their smooth integration into practical applications. This role demands a harmonious blend of theoretical insight and hands-on engineering, focusing on creating dependable, maintainable, and impactful AI-driven features that align with Nace.AI's strategic goals.Key Responsibilities:Develop and sustain complete ML systems, including synthetic data pipelines, model training, debugging, and performance assessment.Enhance large language models (LLMs) and utilize meta-learning strategies to boost model generalization and efficiency.Refine existing Nace.AI models by integrating breakthroughs from the latest ML research.
