Machine Learning Engineer, AI Agents

Superhuman embraces a hybrid working model designed to offer team members the ideal balance of focused work and collaborative, in-person interactions that cultivate trust, innovation, and a vibrant team culture.About SuperhumanSuperhuman, now inclusive of Grammarly, is an AI productivity platform dedicated to unleashing the superhuman potential within everyo…

Join Strava as a Platform Engineer specializing in Generative AI and Machine Learning. In this pivotal role, you will drive the development of innovative platforms that enhance user experiences and push the boundaries of technology. Collaborate with a dynamic team of engineers and data scientists to create scalable solutions that utilize advanced AI techniques. Your work will directly influence the future of our products and services, making a significant impact on athletes and fitness enthusiasts worldwide.

About Us:At Ambience Healthcare, we are not just another scribe; we are pioneering an AI intelligence platform that reinvigorates the human touch in healthcare while delivering significant ROI for health systems nationwide.Our innovative technology enables healthcare providers to concentrate on delivering exceptional care by alleviating the administrative burdens that detract from patient interactions and their most impactful work. Ambience provides real-time, coding-aware documentation and clinical workflow support in ambulatory, emergency, and inpatient settings across leading health systems in North America.Our team is driven by a relentless pursuit of excellence and extreme ownership, dedicated to crafting the best solutions for our health system partners. We champion transparency, positivity, and thoughtful engagement, holding each other accountable because we understand the significance of the challenges we tackle.Ambience has earned accolades such as being ranked #1 for Improving the Clinician Experience in the KLAS Research Emerging Solutions Top 20 Report, being recognized by Fast Company as one of the Next Big Things in Tech, and being named one of the best AI companies in healthcare by Inc. We were also selected as a LinkedIn Top Startup in 2024 and 2025. Our esteemed investors include Oak HC/FT, Andreessen Horowitz (a16z), OpenAI Startup Fund, and Kleiner Perkins — and our journey is just beginning.The Role:As a Staff Machine Learning Engineer, you will play a crucial role in advancing clinical AI that impacts millions of patient encounters across the largest health systems in the nation. Your contributions will directly influence the speed at which we enhance our AI capabilities through the platform you will oversee.You will design and implement evaluation and release processes that empower teams to deliver with confidence, create observability tools to identify quality issues pro-actively, and develop debugging tools that facilitate rapid issue reproduction. Additionally, you’ll work on the chart context retrieval layer that transforms patient history into model-ready inputs.Our goal is to enable teams to iterate on quality within days, not weeks, ensuring that every enhancement you implement adds value across all product teams each quarter.Please note that our engineering roles operate in a hybrid model from our San Francisco office (3 days per week).What You’ll Own:Evaluation & Release Infrastructure — Developing automated grading systems and release gates that function seamlessly across product teams, creating a unified evaluation dataset with version control to replace fragmented workflows. Implementing production-quality monitoring that includes end-to-end tracing, shared metrics, and automated alerts.Debugging Tools — Building encounter replay features that reconstruct precise inference inputs (including retrieved chart context, packed prompts, and model versions) to allow teams to troubleshoot issues without sifting through logs. Creating differential views to compare known good states with regressions.

Be a Part of the Revolution in E-Commerce with Whatnot!Whatnot stands as the leading live shopping platform across North America and Europe, where you can buy, sell, and explore the items you cherish. We are transforming the landscape of e-commerce by merging community engagement, shopping, and entertainment into a unique experience tailored just for you. As a remote-first team, we are driven by innovation and firmly rooted in our core values. With operational hubs in the US, UK, Germany, Ireland, and Poland, we are collaboratively crafting the future of online marketplaces.From fashion and beauty to electronics and collectibles like trading cards, comic books, and live plants, our live auctions cater to a diverse audience.And this is just the beginning! As one of the fastest-growing marketplaces, we are on the lookout for innovative, forward-thinking problem solvers in all areas of our business. Stay updated with the latest from Whatnot through our news and engineering blogs, and join us in empowering individuals to transform their passions into successful ventures while fostering community through commerce. The RoleWe are seeking passionate builders—intellectually curious, entrepreneurial engineers who are ready to pioneer the future of AI and ML at Whatnot. You will be responsible for designing and scaling the foundational infrastructure that supports machine learning and self-hosted large language model applications throughout the organization. Collaborating closely with machine learning scientists, you will facilitate the deployment of cutting-edge models into production, creating entirely new product experiences. Your work will involve constructing systems that ensure advanced machine learning is reliable and efficient at scale—from low-latency model serving to distributed training and high-throughput GPU inference.Your Responsibilities:Lead the infrastructure that powers AI and ML models across vital business domains—enhancing growth, trust and safety, fraud detection, seller tools, and more.Prototype, deploy, and operationalize innovative ML architectures that significantly influence user experience and marketplace dynamics.Design and scale inference infrastructure capable of managing large models with minimal latency and maximal throughput.Construct distributed training and inference pipelines utilizing GPUs, as well as model and data parallelism.Push the boundaries of your expertise and explore new technologies and methodologies.

Join us in creating the backbone of data infrastructure for real-world robotic operations.As robotics transitions from research labs to real-world applications across factories, warehouses, vehicles, and field deployments, understanding the intricacies of robotic performance becomes critical. When robots encounter failures or unexpected behaviors, data analysis is key to deciphering the underlying issues.At Foxglove, we are at the forefront of building tools for observability, visualization, and data infrastructure that empower robotics and autonomous systems teams to manage, analyze, and derive insights from vast amounts of multimodal sensor data collected from operational systems and production fleets.Role OverviewWe are seeking a passionate ML Platform Engineer with robust infrastructure expertise to design, deploy, and scale our data platform systems. This platform-centric role will allow you to take charge of the infrastructure layer that facilitates machine learning in production environments, going beyond just the models themselves.Your responsibilities will encompass ensuring the reliability, scalability, and performance of the ML platform, including areas such as inference serving, pipeline orchestration, training infrastructure, and evaluation frameworks. You will be tackling substantial challenges such as managing petabyte-scale multimodal robotics data and optimizing high-throughput retrieval and embedding pipelines in a hands-on infrastructure capacity.Key ResponsibilitiesDesign and operationalize production inference infrastructure, focusing on model serving, autoscaling, load balancing, and cost efficiency across cloud environments.Own the platform architecture for embedding and retrieval pipelines that enable semantic search across multimodal robotics data (image, video, point cloud, and time series).Develop and sustain the training and evaluation infrastructure that supports rapid model performance iteration, including job orchestration, experiment tracking, and dataset versioning.Lead decisions on cloud infrastructure (AWS/GCP) that affect latency, throughput, reliability, and scalability.Establish platform abstractions and internal tools that empower product engineers to deliver ML-enhanced features without managing infrastructure directly.Assess, integrate, and operationalize third-party ML infrastructure components while establishing clear build vs. buy frameworks for the team.

tvScientific, powered by Pinterest, develops a connected TV (CTV) advertising platform designed for performance marketers. The platform combines media buying, optimization, measurement, and attribution to automate and improve TV advertising. Built by professionals in programmatic advertising, digital media, and ad verification, tvScientific aims to deliver measurable results for advertisers. Role overview As a Machine Learning Platform Engineer, you will join a team that operates where Site Reliability Engineering meets low-latency distributed systems. This team advances Pinterest’s real-time machine learning and measurement infrastructure, focusing on sub-millisecond decision-making and high-throughput data access. Seamless integration with Pinterest’s core stack is central to the work. What you will do Design and build systems to keep queries and RPCs fast and reliable, even during periods of heavy demand. Develop and enhance the foundation of the machine learning training and serving stack. Address challenges in storage, indexing, streaming, fan-out, and managing backpressure and failures across services and regions. Collaborate with software engineering, data infrastructure, and SRE teams to ensure systems are observable, debuggable, and ready for production. Key areas of focus I/O scheduling and batching Lock-free or low-contention data structures Connection pooling and query planning Kernel and network tuning On-disk layout and indexing strategies Circuit-breaking and autoscaling Incident response and failure management NixOS Defining and maintaining SLIs and SLOs This position is a strong fit for engineers interested in building and operating large-scale infrastructure, particularly those who enjoy working on real-time systems, observability, and reliability.

About FaireFaire is a transformative online wholesale marketplace, driven by the conviction that local businesses are the future. Independent retailers around the globe generate more revenue than massive corporations like Walmart and Amazon combined, yet individually, they remain small. At Faire, we harness technology, data, and machine learning to connect this vibrant community of entrepreneurs. Think of your favorite local boutique — we empower them to discover and sell the best products from around the world. With our innovative tools and insights, we aim to level the playing field, enabling small businesses to thrive against larger competitors.By championing the growth of independent businesses, Faire positively impacts local economies on a global scale. We’re in search of intelligent, resourceful, and passionate individuals to join us in fueling the shop local movement. If you value community, we invite you to be part of ours.About this RoleAs the Senior Staff Machine Learning Platform Engineer, you will spearhead the technical vision and evolution of Faire's ML platform. You will establish standards, influence organization-wide architecture, and lead intricate, cross-functional initiatives that enhance data science velocity at scale. This position is crucial for adapting ML workflows to leverage modern AI productivity tools. You will not only develop models but also design the systems that enable those models to empower tens of thousands of small retailers in competing and growing their local businesses.

About Scale AI At Scale AI, we are dedicated to propelling the advancement of AI applications. Over the past eight years, we have established ourselves as the premier AI data foundry, supporting groundbreaking innovations in fields such as generative AI, defense technologies, and autonomous vehicles. Following our recent Series F funding round, we are intensifying our efforts to harness frontier data, paving the way toward achieving Artificial General Intelligence (AGI). Our work with enterprise clients and governments has enhanced our model evaluation capabilities, allowing us to expand our offerings for both public and private evaluations. About the ACE Team The Agent Capabilities & Environments (ACE) team, a vital part of Scale’s Research organization, unites customer-focused Researchers and Applied AI Engineers. Our primary mission is to conduct research on agent environments and reinforcement learning reward signals, benchmark autonomous agent performance in real-world contexts, and develop robust data programs aimed at enhancing the capabilities of Large Language Models (LLMs). We are committed to creating foundational tools and frameworks for evaluating models as agents, focusing on autonomous agents that interact dynamically with a wide range of external environments, including code repositories and GUI interfaces. About This Role This position sits at the cutting edge of AI research and its practical applications, concentrating on the data types necessary for the development of state-of-the-art agents, including browser and software engineering agents. The ideal candidate will investigate the data landscape required to propel intelligent and adaptable AI agents, steering the data strategy at Scale to foster innovation. This role demands not only expertise in LLM agents and planning algorithms but also creative problem-solving skills to tackle novel challenges pertaining to data, interaction, and evaluation. You will contribute to influential research publications on agents, collaborate with customer researchers, and partner with the engineering team to transform these advancements into scalable real-world solutions.

Join Whatnot as a Machine Learning Platform Engineer, where you'll play a pivotal role in shaping the future of our AI-driven solutions. In this dynamic position, you will collaborate with cross-functional teams to design, implement, and optimize machine learning platforms that drive efficiency and innovation.Your expertise will be critical in enhancing our data processing capabilities and deploying robust machine learning models at scale. If you are passionate about leveraging cutting-edge technology to solve complex challenges, we want to hear from you!

Join Databricks Mosaic AI as a Senior Machine Learning Engineer and take the lead in developing our cutting-edge generative AI platform. Our team, formed in late 2020, empowers businesses by allowing them to securely fine-tune, train, and deploy custom AI models using their own data. This ensures maximum security and control while being compatible with all major cloud providers, allowing for unparalleled flexibility in AI development.Since our integration into Databricks in July 2023, we have been dedicated to tackling some of the world's most challenging problems, from revolutionizing transportation to accelerating medical advancements. We leverage deep data insights to enhance our customers' business capabilities and thrive on overcoming technical challenges to deliver superior data and AI solutions.Role Overview:As a Senior Machine Learning Engineer, you will play a pivotal role in the design and implementation of our generative AI platform, covering the entire ML development lifecycle, including data generation, training, evaluation, serving, and agent-building. Your expertise will be essential in translating user requirements into intuitive product interfaces while constructing robust backend distributed systems that drive these features.

Saris AI, based in San Francisco with teams in Montreal and Toronto, develops advanced agentic AI systems for the banking industry. The company focuses on automating complex workflows that require long-context reasoning, integration with legacy systems, and strict compliance. With live AI agents already supporting real customer operations, Saris AI is expanding quickly and seeking technical leaders who want to shape the future of work in banking. Role overview This is a hands-on leadership position within the core engineering team in San Francisco. The Machine Learning Engineering Lead will guide machine learning systems from initial concept through scaling, helping define both the technical vision and the supporting infrastructure. What you will do Oversee the ML/AI function end to end, setting technical direction and standards across the company. Design and supervise development of multi-modal, agentic AI systems that power live customer workflows. Build and manage evaluation frameworks, datasets, and metrics to improve agent performance. Drive productionization of ML systems with an emphasis on reliability, scalability, and compliance. Recruit, develop, and mentor a high-performing ML team, fostering strong practices in modeling, experimentation, and deployment. Requirements 8+ years of experience in machine learning or AI engineering, including time as a technical lead or manager. Proven track record leading ML projects from concept to production deployment. Expertise with large language models (LLMs) and/or agentic systems, especially in customer-facing products. Strong grasp of ML fundamentals: deep learning, transformers, model evaluation, and trade-offs. Hands-on experience scaling ML systems in production, with a focus on monitoring, iteration, and reliability. Ability to lead engineering teams, influence architecture, and set technical direction. Comfort working in early-stage, ambiguous, and rapidly changing environments.

Artificial Intelligence is increasingly becoming a pivotal element across all sectors of society. At Scale AI, we are committed to accelerating the evolution of AI applications. For nearly a decade, we have been the premier AI data foundry, propelling groundbreaking advancements in areas such as generative AI, defense applications, and autonomous vehicles. Following our recent investment from Meta, we are intensifying our efforts to develop advanced post-training algorithms that are essential for sophisticated agents in enterprises worldwide.The Enterprise ML Research Lab is at the forefront of this AI revolution, leveraging a suite of proprietary research, tools, and resources to support our enterprise clients. As a Staff Machine Learning Research Engineer focusing on Agent Post-training, you will be instrumental in creating our next-generation Agent Reinforcement Learning training platform. Your work will enable the training of top-tier Agents that deliver state-of-the-art results in real-world enterprise applications.You will incorporate cutting-edge research into our training framework, empowering ML Research Engineers on the Enterprise AI team to deploy use cases ranging from next-generation AI cybersecurity firewalls to training foundational healthtech search models. If you are passionate about shaping the future of the GenAI movement, we welcome your application!

About UsAt Citizen Health, we believe that the right advocate can significantly enhance healthcare experiences and outcomes. Founded on the principles of personal healthcare journeys, we leverage a unique combination of data, artificial intelligence, and community engagement to craft a personalized AI advocate. Our platform harnesses patients' comprehensive medical histories alongside data from a vast network of individuals, providing tailored insights for effective clinical decisions and everyday challenges. We focus initially on rare and complex conditions, allowing patients to share their information for mutual benefit, while empowering biopharma and researchers with regulatory-grade data that accelerates the drug development process for critical treatments.Our team consists of seasoned entrepreneurs with successful track records, backed by esteemed investors such as 8VC, Transformation Capital, and Headline Ventures. We are passionate about reshaping the future of consumer healthcare.Position OverviewCitizen Health is on the lookout for talented AI/Machine Learning Engineers to spearhead the development and implementation of innovative AI solutions for our patient-centered platform. This pivotal role involves crafting and deploying advanced machine learning models that convert intricate health data into actionable insights for patients, healthcare professionals, and researchers.As a vital technical leader, you will be at the cutting edge of applying sophisticated machine learning methodologies to tackle complex challenges in rare disease research and patient care. Your contributions will be crucial in developing AI-driven solutions that enhance disease comprehension, treatment options, and overall patient outcomes.Key ResponsibilitiesDesign and execute comprehensive machine learning solutions, covering data preprocessing to model deployment and ongoing monitoring.Develop and refine advanced Large Language Models (LLMs) tailored for healthcare applications, utilizing techniques such as fine-tuning and Retrieval-Augmented Generation (RAG).Construct robust data pipelines for validation and deployment processes.Implement machine learning systems capable of processing and analyzing diverse healthcare data types, including structured clinical data, medical imaging, and unstructured text.Collaborate closely with backend engineers to seamlessly integrate ML models into our production infrastructure.Ensure that ML systems adhere to rigorous healthcare compliance standards while maintaining optimal performance.

Innovate Boldly. Shape Tomorrow. Our VisionCrafting everyday AGI. Reliable, consumer-friendly agents that transform human-AI synergy for millions. Our software is designed to act as a collaborator, enhancing your daily capabilities.Why Choose AGI, Inc.?We are a discreet collective of exceptional founders and AI pioneers, whose expertise spans Stanford, OpenAI, and DeepMind. Our team leads the way in mobile and computer-based agents, scaling these innovations for consumer use.With a foundation rooted in extensive research on agents, our AI prioritizes trustworthiness and reliability as fundamental principles.Backed by top-tier investors who previously supported the first wave of AI leaders, we are now positioned to create the next generation: everyday AGI. (Check out the demo)If you envision possibilities where others perceive restrictions, continue reading.Your RoleTraining Automation: Design and execute robust CI/CD pipelines tailored for machine learning workflows. Automate nightly and on-demand training sessions encompassing data ingestion, job orchestration, checkpointing, and artifact management, with a focus on reliability.Evaluation Infrastructure: Develop scalable evaluation frameworks that automatically benchmark models with each merge. Enhance latency and resource efficiency to ensure quick experimentation and immediate detection of performance regressions.Research Tooling: Create internal SDKs, CLIs, and lightweight UIs (e.g., Streamlit, Retool) empowering researchers to:Examine trajectories and tracesVisualize model failuresOrganize and oversee datasetsIterate seamlesslyYou'll facilitate a user-friendly experimentation process.Observability & Performance: Enforce comprehensive tracking for:Model latency, throughput, and error ratesGPU utilization, and more.

The RoleJoin Moveworks as a Senior Machine Learning Engineer II, where you will play a crucial role in enhancing our natural language understanding (NLU) and agentic AI capabilities. Your expertise in machine learning will help us deliver seamless and intelligent user experiences across our generative and conversational AI platforms.As part of our dedicated NLU team, you'll leverage cutting-edge NLP and NLG tools, including state-of-the-art LLMs, multimodal foundation models, and hybrid vector databases. Our infrastructure empowers you to fine-tune, evaluate, and deploy your models effectively in a production environment. Collaborating closely with our world-class annotation team, you will create accurate, inclusive, and privacy-conscious datasets for model training and assessment.Your role extends beyond model training; you will be integral in achieving exceptional AI performance across various metrics, including response speed, reliability, and overall user system capabilities. Our successful engineers are equally passionate about designing robust AI systems as they are about model development.Our team values agility in addressing complex product and engineering challenges, continually striving to enhance the value delivered to our clients. Your contributions will significantly influence our mission to decode enterprise challenges and develop a highly reliable AI copilot through collaborative efforts across the organization.

At Yutori, we are revolutionizing the way individuals engage with the online world by developing AI agents that can seamlessly manage everyday digital tasks. Our mission is to create a fully integrated agent-first ecosystem, encompassing everything from training proprietary models to designing intuitive generative product interfaces.We invite a passionate and skilled AI Engineer to join our founding team and contribute to our vision of building superhuman AI agents capable of performing actions across the web.Our founders—Devi Parikh, Abhishek Das, and Dhruv Batra—bring decades of expertise in AI research and product development from their tenure at Meta, focusing on generative, multimodal, and embodied AI. Our diverse team blends advanced AI knowledge with innovative product design to execute Yutori's ambitious mission.Supported by an exceptional group of visionary investors—including Elad Gil, Sarah Guo, Jeff Dean, Fei-Fei Li, and others—Yutori is poised for remarkable growth and development.

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.

About Prophecy At Prophecy, we are at the forefront of AI-driven data preparation and analysis, transforming how leading enterprises convert data chaos into actionable insights. Our innovative AI-native data lifecycle—encompassing generation, refinement, and deployment—facilitates seamless collaboration between our advanced AI agents and skilled human analysts through intuitive visual and document interfaces. Join us in our mission to deliver trustworthy insights on an enterprise scale and be part of the next data revolution.

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.

About Liquid AIFounded as a spin-off from MIT CSAIL, Liquid AI specializes in creating versatile AI systems designed for optimal performance across various deployment platforms, including data center accelerators and on-device hardware. Our technology emphasizes low latency, minimal memory consumption, privacy, and dependability. We collaborate with leading enterprises in sectors such as consumer electronics, automotive, life sciences, and financial services. As we experience rapid growth, we are on the lookout for exceptional talent to join our team.The OpportunityThe Data team at Liquid AI drives the development of our Liquid Foundation Models, focusing on pre-training, vision, audio, and emerging modalities. With the stagnation of public data sources, the effectiveness of our models increasingly relies on specially curated datasets. We are seeking engineers with a machine learning mindset who can efficiently gather, filter, and synthesize high-quality data at scale.At Liquid AI, we regard data as a research challenge rather than an infrastructural issue. Our engineers conduct experiments, design ablations, and assess how data-related decisions impact model quality. We will align you with a team where you can experience rapid growth and make a significant impact, be it in pre-training, post-training reinforcement learning, vision-language, audio, or multimodal applications.While we prefer candidates in San Francisco and Boston, we are open to considering other locations.What We're Looking ForWe are in search of a candidate who:Thinks like a researcher and executes like an engineer: You should be able to formulate hypotheses, conduct experiments, and evaluate results. Our engineers produce research-level code while our researchers implement production systems.Learns quickly and adapts: You will be working in rapidly evolving modalities, so the ability to quickly grasp new domains and thrive in ambiguity is essential.Prioritizes data quality: We hold data quality in high regard; tasks such as filtering, deduplication, augmentation, and evaluation are key responsibilities, not afterthoughts.Solves problems autonomously: Data engineers operate within training groups (pre-training and multimodal). While collaboration is crucial, we expect ownership and self-direction.The WorkDevelop and maintain data processing, filtering, and selection pipelines at scale.Establish pipelines for pretraining, midtraining, supervised fine-tuning, and preference optimization datasets.Design synthetic data generation systems utilizing large language models (LLMs), structured prompting, and domain-specific generative techniques.