Machine Learning Engineer - ETA Team

Join our ETA Team at DoorDash as a Machine Learning Engineer. In this dynamic role, you will harness the power of machine learning to drive innovation and optimize our delivery processes. You will collaborate with cross-functional teams to develop and deploy scalable machine learning models that enhance customer experience and operational efficiency.

About Scribd:At Scribd Inc. (pronounced “scribbed”), we're on a mission to ignite human curiosity and foster a world enriched with stories and knowledge. As part of our dynamic team, you'll contribute to democratizing the exchange of ideas and empowering collective expertise through our innovative products: Everand, Scribd, Slideshare, and Fable.This job posting reflects an open and approved position within our organization.We cultivate a culture where authenticity and boldness are celebrated; where we engage in thoughtful debate and commit to embracing unexpected challenges; and where every team member is empowered to take initiative with the customer at the forefront of our priorities.Our workplace structure emphasizes a balance between individual flexibility and community engagement. With our Scribd Flex benefit, employees—together with their managers—can choose the work style that best fits their personal needs. We prioritize intentional in-person interactions to enhance collaboration, culture, and connection, which is why occasional in-office attendance is required for all Scribd employees, regardless of their work location.What are we looking for in new team members? We value “GRIT,” defined as the intersection of passion and perseverance towards long-term goals. At Scribd Inc., we believe this can unlock immense potential in our employees. We seek individuals who are adept at setting and achieving Goals, delivering Results in their responsibilities, providing Innovative solutions, and positively impacting the broader Team through collaboration and a positive attitude.About the Team:Our Machine Learning team is dedicated to developing the platform and product applications that drive personalized discovery, recommendations, and generative AI capabilities across Scribd, Slideshare, and Everand. The ML team works on the Orion ML Platform, which provides essential ML infrastructure, including a feature store, model registry, model inference systems, and embedding-based retrieval (EBR). They collaborate closely with the Product team to deliver seamless integrations of ML into user-facing features such as recommendations and real-time personalization.

Join Us in Building a Safer Financial System.At TRM Labs, we are at the forefront of blockchain analytics and AI technology, dedicated to empowering law enforcement, national security, financial institutions, and cryptocurrency businesses in the fight against crypto-related fraud and financial crime. Our advanced platforms leverage blockchain intelligence and AI to trace the flow of funds, identify illicit activities, build robust cases, and provide a comprehensive understanding of threats. Trusted globally, TRM Labs is committed to creating a safer and more secure environment for everyone.Our mission is to develop an innovative financial system that benefits billions around the globe. By integrating threat intelligence with machine learning, our next-generation platform enables institutions and governments to detect cryptocurrency fraud and financial crimes on an unmatched scale.As a Machine Learning Infrastructure Engineer at TRM Labs, you will collaborate with a talented team of data scientists, engineers, and product managers. Your role will involve designing and maintaining scalable GPU-powered infrastructure that supports our AI systems. You will work at the intersection of distributed systems, cloud infrastructure, and applied machine learning, laying the groundwork for high-throughput, production-level ML workloads.

Saris AI develops applied AI solutions for the banking sector, with teams in San Francisco, Montreal, and Toronto. The company builds automation tools that handle complex, long-context reasoning and agent-driven decision-making. Reliability and compliance shape every product, and Saris AI's agents already manage real customer workflows in production. As revenue grows, the engineering team is expanding to enhance current offerings and explore new directions. The Senior Machine Learning Engineer role is based in San Francisco and sits within the core engineering group. The team works in a collaborative, early-stage setting, balancing infrastructure needs with the delivery of features that serve customers directly. What you will do Build and maintain machine learning infrastructure, such as evaluation frameworks, prompt management systems, and tools for model observability. Develop new AI features for customers while supporting and improving the underlying infrastructure. Shape strategies for evaluation, LLM routing, prompt engineering, and model selection. Set practical standards to boost quality without slowing down development. Guide technical direction by clarifying trade-offs and architectural choices. Requirements Minimum 4 years of experience in machine learning or AI engineering, including production deployment of ML systems. Direct experience with large language models, prompt engineering, evaluation techniques, and model routing. Background in building tools and systems that deliver value to users. Comfort making pragmatic trade-offs and recognizing when a solution is sufficient. Ability to navigate ambiguity, define problems, and deliver results independently. Strong focus on end users and understanding the impact of ML decisions on customer experience. Supports team growth through code reviews, collaboration, and clear technical communication. Bonus Experience in regulated industries, especially banking.

Join us in creating the most dependable on-demand logistics engine for last-mile retail delivery! We are on the lookout for a seasoned machine learning engineer to aid in the development of cutting-edge growth and personalization models that will elevate DoorDash's expanding retail and grocery services.About the RoleWe are seeking a dedicated Applied Machine Learning expert to become part of our innovative team. As a Staff Machine Learning Engineer, you will conceptualize, design, implement, and validate algorithmic enhancements that enrich the growth and personalization experiences central to our rapidly evolving grocery and retail delivery business. Leveraging our advanced data and machine learning infrastructure, you will implement novel ML solutions to enhance the consumer search experience, making it more relevant, seamless, and enjoyable across grocery, convenience, and various retail sectors. A strong command of production-level machine learning and proven experience in addressing end-user challenges while collaborating effectively with multidisciplinary teams is essential.This position will report to the engineering manager on our Personalization team and is expected to be hybrid, combining both in-office and remote work (#LI-Hybrid).

The Senior Machine Learning Engineer at Scale AI is tasked with harnessing advanced methodologies in generative AI, computer vision, reinforcement learning, and agentic AI to enhance our product offerings and streamline the customer experience in live environments. Our machine learning engineers utilize a robust internal infrastructure and unparalleled access to extensive datasets to drive significant improvements for our clients. As part of the Public Sector Machine Learning team, you will be at the forefront of deploying innovative models to critical government systems through groundbreaking products such as Donovan and Thunderforge. Our work encompasses various modalities, with a strong emphasis on large language models and computer vision. On the LLM front, we are developing sophisticated agentic systems designed to address intricate operational and planning challenges for government entities. This involves creating agent frameworks that interface with custom retrieval systems and production APIs, alongside evaluation tools for assessing and refining agent performance. Additionally, we are pioneering research in reinforcement learning for agentic LLMs, successfully deploying these innovations in real-world operational contexts. In the realm of computer vision, we are focused on training advanced models to boost labeling throughput and automate perception tasks. Our initiatives include developing large-scale fine-tuning pipelines, training models across diverse modalities, and creating adaptable vision foundation models to support a variety of defense applications.

About ApiphanyApiphany is an innovative AI company dedicated to advancing physical product development. We empower global leaders in industries such as automotive, aerospace, medtech, and energy to convert vast amounts of unstructured technical data into immediate, actionable insights. Supported by elite investors including Markforged, Databricks, GM, and Character, our mission is to transform engineering decision-making, simplifying complexity for the world's premier manufacturers.Our models are meticulously crafted to address the unique challenges of engineering and manufacturing. They are designed to comprehend principles of physics, design specifications, and program constraints. Our team is a select group of experts from prestigious institutions like Stanford, Berkeley, MIT, UW, and CMU, alongside veterans from GM, Ford, and Genesis Therapeutics. We are committed to redefining hard-tech and constructing a category-defining enterprise together.About the RoleAs a Machine Learning Engineer at Apiphany, you will architect and deploy cutting-edge machine learning models to address some of the most intricate challenges within the physical domain. You will create systems capable of reasoning with complex engineering data, developing AI that grasps physics, design limitations, and real-world performance trade-offs.This role is tailored for innovators eager to expand the horizons of AI applications in the tangible world.

Join our innovative DashPass team at DoorDash as a Senior Machine Learning Engineer, where you'll play a pivotal role in harnessing AI and advanced machine learning to revolutionize subscriber personalization strategies. In this dynamic position, you will lead the design and development of large-scale ML systems that enhance user engagement and retention across the DashPass subscriber journey. Your expertise in causal inference modeling and incentive optimization will drive impactful decisions, ensuring our subscribers receive tailored rewards and promotions that resonate with their preferences. Collaborate closely with cross-functional teams to create experiments and production ML systems that directly influence growth metrics. This is a unique opportunity to be at the forefront of personalization efforts that will shape the future of our offerings.

Airbnb began in 2007 with two hosts and three guests in San Francisco. Since then, the platform has grown to over 5 million hosts and more than 2 billion guests worldwide. Airbnb connects people with unique places to stay and experiences, building authentic community connections across nearly every country. The team: Growth Platform Engineering The Growth Platform team focuses on driving sustainable, long-term growth for Airbnb. The team’s mission centers on building an agentic system and supporting capabilities to help all Airbnb offerings grow, both now and in the future. Efforts include delivering personalized and relevant content and product experiences to users, both on and off the Airbnb platform. The team is working toward a future where AI identifies opportunities, creates campaigns, personalizes experiences, and optimizes outcomes with minimal human input. This journey moves through a maturity curve: AI-assisted, agentic, and ultimately autonomous systems, always with human oversight to ensure brand safety, quality, and compliance. Growth Platform Engineering is tightly integrated with the Airbnb product, enhancing the customer journey and enabling new ways for users to engage. The platform supports a range of digital marketing channels, landing pages, email, push notifications, SMS, and digital advertising, as well as the machine learning and data infrastructure that powers these efforts. What you will do Develop AI-driven solutions to shape the future of Airbnb’s agentic growth platform, using the latest AI methodologies. Lead and mentor engineers through brainstorming, design, and implementation of AI products and features, from initial concept to deployment. Work at the intersection of technical depth, architectural innovation, and mentorship as a Senior Staff Engineer. Collaborate with cross-functional teams to build scalable systems that operate globally. Help evolve the foundational elements of Airbnb’s AI-powered growth systems.

Join Anthropic as a Machine Learning Systems Engineer within our Encodings and Tokenization team, where you'll play a pivotal role in refining and optimizing our tokenization systems across Pretraining and Finetuning workflows. By bridging the gap between our Pretraining and Finetuning teams, you will help shape the essential infrastructure that enhances how our AI models learn from diverse data. Your contributions will be crucial in ensuring our AI systems remain reliable, interpretable, and steerable, driving forward our mission of developing beneficial AI technologies.

At Lyft, we are driven by our mission to connect and serve our communities. We strive to foster a workplace where every team member feels valued and has the opportunity to excel. With over half a billion rides and counting, Lyft is tackling complex challenges on a grand scale, utilizing cutting-edge AI and Machine Learning technologies to enhance customer experiences. The Artificial Intelligence, Machine Learning, and Operations Research Platforms team (AIMLOR) is on the lookout for a Senior Machine Learning Engineer who will play a pivotal role in constructing AI Platform components that empower essential AI applications across Lyft. Mastery in Generative AI and platform development is crucial for this position. You will contribute to our platform that facilitates real-time, online, and offline AI and ML model execution, development, and iteration. Collaborating with a team of talented Machine Learning and Software Engineers, you will work on intricate problems and define solutions that make a direct impact on our systems throughout the organization. If you are enthusiastic about building an AI Platform at scale with applications spanning every aspect of our company, we want to hear from you. If you are a creative thinker with a strong background in AI and machine learning systems and are passionate about leveraging data to solve business challenges in a dynamic, innovative, and collaborative environment, we invite you to apply.

Supported by top-tier Silicon Valley investors, Peregrine Technologies empowers public safety organizations, local and state governments, federal agencies, and private entities to tackle societal challenges with unmatched speed and precision. Our AI-driven platform transforms fragmented and isolated data into actionable operational intelligence, enabling immediate access to critical information that enhances decision-making processes. Currently, Peregrine serves hundreds of clients across more than 30 states and two countries, positively impacting over 125 million individuals as we extend our influence into the enterprise sector and beyond.TeamWe believe that empathy is key in engineering. Understanding how users engage with our product is vital to our success. Our engineers collaborate closely with on-site teams to grasp the diverse use cases that Peregrine addresses.We prioritize both ownership and teamwork, encouraging you to take accountability for significant features while working alongside fellow engineers to bring them to fruition. We value humility and empathy as essential traits in developing effective solutions, engaging directly with our deployment teams and users to iteratively resolve their challenges. Creativity and perseverance are critical to realizing our vision.RoleAs a crucial member of our engineering team, you'll play a pivotal role in delivering exceptional value to our customers. This team focuses on creating robust, user-friendly experiences powered by generative AI. You will pioneer innovative interactions for users within our platform, shaping impactful AI-driven features that assist clients in solving real-world problems swiftly and efficiently.Your responsibilities will encompass addressing a variety of complex challenges, including scaling our platform to manage terabytes of data from multiple sources, providing real-time user notifications and queries, and optimizing search algorithms for rapid result delivery.Our technology stack is continually evolving, anchored by a backend built with Python, Django, Celery, Airflow, and Kafka; a frontend utilizing React, Redux, and Mapbox; data storage solutions including PostgreSQL and Elasticsearch; machine learning models hosted in Bedrock and SageMaker; along with AWS, Pulumi, Terraform, and Kubernetes forming our infrastructure.About YouEnthusiasm and ambition to take ownership of major projects and contribute to the team's success.

Join Patreon as a Staff Machine Learning Engineer specializing in Taxonomy. In this pivotal role, you will leverage your advanced knowledge in machine learning and data science to enhance our taxonomy systems, providing value to creators and patrons alike. You will work collaboratively with cross-functional teams to design and implement innovative solutions that drive our mission forward.

At Scale AI, our Physical AI division is at the forefront of addressing data challenges in Robotics, Autonomous Vehicles, and Computer Vision. We invite you to join our team as a Machine Learning Systems Engineer, where you will play a pivotal role in applied research and the development of machine learning pipelines. Your focus will be on enhancing algorithms and pipelines for optimal performance on cloud-based GPU systems, empowering advancements in Physical AI research and applications.Your Role:As a Machine Learning Systems Engineer within the Physical AI team, you will design and implement robust platforms that ensure the scalable and efficient deployment of foundational models for physical agents. Your contributions will support groundbreaking research and production systems, facilitating internal discoveries and external applications in the fields of robotics and autonomous technology.We seek candidates who possess a strong foundation in machine learning coupled with extensive backend system design expertise. You will thrive in a collaborative environment, bridging the gap between Physical AI research and production engineering to expedite innovation across Scale AI.

Join our innovative team at mercor as a Machine Learning Engineer specializing in Anonymization. In this role, you will harness the power of artificial intelligence to develop cutting-edge algorithms and solutions that protect user privacy while maintaining data utility. You will work closely with data scientists and software engineers to create advanced systems that anonymize sensitive data efficiently and effectively.

Lyft’s Business team is hiring a Machine Learning Engineer to develop algorithms and models that improve operations and customer experience. This position plays a key part in shaping how Lyft’s services perform by turning data into actionable insights. What you will do Design and build machine learning models to support Lyft Business operations Develop algorithms that enhance service quality and efficiency Work with data to uncover insights that inform business decisions Location This role is based in New York, NY or San Francisco, CA.

Role Overview Adaption is hiring a Forward Deployed Machine Learning Engineer in San Francisco. This role focuses on applying advanced machine learning methods to address practical challenges. The work involves both client-facing projects and collaboration with internal teams. What You Will Do Work directly with clients to understand their needs and translate them into technical solutions Develop and deploy machine learning models to improve operational processes Collaborate with colleagues across disciplines to deliver impactful results

Founding Machine Learning EngineerLocation: San Francisco, CA Work Model: In-office 5 days a weekAbout UsAt Effective AI, we are pioneering the future of work. Our vision is to push the boundaries of AI beyond mere repetitive tasks, focusing instead on intricate knowledge work that requires expertise and multi-faceted reasoning. We are developing advanced AI Teammates that are designed to navigate complex workflows and collaborate seamlessly with human professionals. Our initial focus is on the trillion-dollar U.S. Property & Casualty insurance sector, a domain rich with complexity and data, making it an ideal arena for our innovations.We proudly secured $10 million in seed funding from prominent investors including Lightspeed Ventures and Valor Equity Partners.Our committed team is based in San Francisco and thrives on in-person collaboration to tackle these significant challenges.Your RoleAs a Founding Machine Learning Engineer, you will be an integral member of our founding team, responsible for architecting, training, and deploying the agent loops that power our AI Teammates from inception. You will address some of the most pressing challenges in agentic AI and natural language processing, developing AI solutions adept at performing essential insurance functions such as underwriting and claims processing.Your responsibilities will include:Architecting and Developing Core ML Pipelines: Design, train, and fine-tune cutting-edge language models (including reinforcement learning agents) to facilitate long-term task accomplishment and complex decision-making.Implementing Nuanced Reasoning: Integrate machine learning techniques that empower agents to make informed decisions based on ambiguous or incomplete data, akin to human expert reasoning and generalization.Building Intelligent, Tool-Using Agents: Engineer the ML systems that enable our agents to dynamically select and utilize a broad array of external tools—including APIs, databases, web searches, and Excel-based pricing algorithms—to gather necessary information and execute actions.Designing and Implementing Robust Evaluation Frameworks: Create and employ comprehensive evaluation metrics and systems to rigorously assess and benchmark agent performance, identify areas for enhancement, and guarantee reliability and safety in real-world insurance processes.Enabling Continuous Adaptation and Learning: Develop resilient ML pipelines and feedback loops that facilitate ongoing learning and adaptation.

The OpportunityJoin us at ComfyOrg as a Senior/Staff Applied Machine Learning Engineer! We are on the hunt for a passionate innovator who is enthusiastic about optimizing model inference. You will play a pivotal role in developing the heart of ComfyUI, our cutting-edge visual AI platform. Your expertise will help us push the limits of AI model performance, making them run faster and more efficiently than ever before.Are You a Match?You are fascinated by model inference, memory management, and torch optimizations.You possess experience in writing production-level PyTorch code that challenges performance standards.You have a passion for understanding the inner workings of AI models.You thrive on developing highly optimized code that consistently delivers results.You believe that the current landscape of ML deployment holds significant room for improvement.Your Responsibilities:Develop and enhance the core inference engine that drives ComfyUI.Optimize large models for speed and memory efficiency.Collaborate with our core team to architect new features.Tackle complex technical challenges within the visual AI domain.Contribute to the future direction of our technology.Experience with diffusion or LLM models, as well as creating custom nodes for ComfyUI, is highly beneficial.

Job OverviewJoin Eragon as a Machine Learning Engineer and lead the charge in transforming innovative AI models into scalable, production-grade systems. This position is pivotal in bridging research and real-world applications by designing and optimizing systems that enhance vital workflows throughout the enterprise.In collaboration with our research, product, and engineering teams, you will convert cutting-edge capabilities into dependable, high-performance systems ready for production.Key ResponsibilitiesModel Development & Deployment: Craft, refine, and deploy machine learning models within production settings.Systems Engineering: Architect scalable pipelines for training, inference, evaluation, and comprehensive monitoring.Performance Optimization: Enhance the latency, throughput, cost-efficiency, and reliability of ML systems.Data & Infrastructure: Manipulate large datasets and ensure seamless integration of models with internal systems and APIs.Cross-Functional Collaboration: Collaborate with product and engineering teams to provide end-to-end AI functionalities.Evaluation & Monitoring: Develop robust evaluation frameworks and feedback loops to ensure system effectiveness.