Staff Research Scientist - Foundation & World Models

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g., Seaborn , Luigi ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're building a platform that covers the whole life of an LLM: training it, deploying it, and observing it in production. We already run multi-node training, elastic inference, sandboxes, and distributed volumes, and we control the infrastructure underneath. We’re looking for research depth in post-training to sit alongside our systems and product work. What You'll Do: We are looking for research scientists with a strong track record in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This role is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustness in high-stakes real-world deplo

As a Staff Research Scientist within Datadog AI Research (DAIR), you will drive research in post-training and autonomous agents as a hands-on individual contributor. You will advance reinforcement learning, simulated environments, synthetic data generation, and evaluation methods for agents operating across complex real-world systems. You will set the technical direction for ambitious research programs, raise the technical bar for the researchers and research engineers around you, and collaborate with Datadog's product and engineering teams to translate research advances into products. What You'll Do: Drive research in agent post-training and reinforcement learning, shaping the technical direction of ambitious research programs grounded in observability and security Own research problems end to end, from framing the question through experimentation, model development, and evaluation Design and advance reinforcement learning approaches, post-training methods, and training loops for agents operating in complex environments Build simulated environments, synthetic data generation approaches, and evaluation frameworks that enable scalable agent training and rigorous measurement Raise the technical bar across the team by reviewing research directions, mentoring researchers and research engineers, and setting standards for experimental rigor Collaborate with cross-functional teams across Research, Product, and Engineering to translate advances in autonomous agents into scalable Datadog capabilities Contribute to research publications, present at top-tier conferences such as NeurIPS, ICLR, and ICML, and help open-source key model artifacts and benchmarks Who You Are: You hold a PhD in Computer Science, Machine Learning, or a related field, or have equivalent experience, with deep expertise in areas such as reinforcement learning, AI agents, post-training, or generative modeling You have driven technically ambitious research at meaningful scale as

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Our Inference team is responsible for building and scaling the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry’s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators. The team has a dual mandate: maximizing compute efficiency to reliably serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms. Inference systems are highly performance sensitive distributed systems. Inference serves hundreds of thousands of customers every day, and the size & span of the inference fleet requires sophisticated routing, scaling, and networking systems. Key responsibilities Design, build, and maintain the distributed systems that serve Claude to millions of users worldwide Develop resilient, flexible systems that adapt in real time to real world events Develop intelligent request routing, load balancing, and traffic management systems across thousands of accelerators and multiple cloud providers Maximize compute efficiency and optimize cost across the fleet by autoscaling and orchestrating production, research, and experimental workloads across multiple cloud providers Build and operate production-grade deployment pipelines for releasing new models to user

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them. The Surfaces Podcasts team owns podcast recommendations across some of Spotify’s most visible surfaces, including Home and the Now Playing view. We build and improve the recommendation systems that help listeners discover their next favorite show or episode while helping creators reach the right audiences. As a Staff Machine Learning Engineer, you’ll help shape the future of personalized discovery and engagement at Spotify. You’ll work at the intersection of recommendation systems, large language models, and production-scale machine learning infrastructure to build experiences for listeners at global scale. You’ll take models from research to production, help set technical direction in ambiguous problem spaces, and solve complex personalization challenges across Spotify. What You'll Do Own and improve the machine learning models and systems that power the Home feed, including the Shortcuts experience. Design, build, and ship personalized recommendations that serve millions of Spotify listeners globally. Build content recommendation systems for emerging agentic and AI-powered user experiences. Train, fine-tune, evaluate, and optimize large language models using techniques such as supervised fine-tuning (SFT), distillation, and parameter-efficient training approaches. Partner closely with Product Managers, Engineers, Data Scientists, and Designers to define and execute experimentation strategies. Drive A/B testing, monitoring, model evaluation, and continuous optimization of recommendation quality, reliability, and cost efficiency.

About Pinecone: Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io . About the Role/Team: As a Technical PMM , you will help developers, software engineers, and machine learning scientists understand how Pinecone enables knowledgeable AI applications. You will accomplish this through messaging and positioning, GTM strategy, launch planning, and enablement for key product features. You will work closely with Product, Sales, Developer Relations, and Marketing teams to ensure our customers understand the value of our platform. Responsibilities: Product Launches & GTM Strategy: Develop launch plans for product releases, align with product teams, and manage GTM execution. Lead core launch teams with cross-functional members (documentation , communications, developer relations, product, growth marketing, etc). Measure customer impact and adoption post-launch, iterating on strategies as needed. Positioning & Messaging: Craft compelling, technically accurate messaging for AI-powered search and retrieval solutions. Build product narratives for enterprise and developer audiences that differentiate Pinecone from competitors. Develop and maintain positioning for new product capabilities Customer & Market Insights: Partner with customers to develop case studies, gathering business impact metrics and architectural insights. Engage in customer interviews and research to refine messaging and identify key value drivers. Content Development & Enablement: Develop internal and external enablement materials, including sales training, pitch decks, and GTM enablement sessions. Collaborate with marketing to create social posts, blogs, and technical content. Requirements: 7+ years of experi

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role At Anthropic, we're building AI systems that are safe, beneficial, and transformative. Our mission is to develop AI that benefits humanity, and we believe the most powerful capabilities emerge when we thoughtfully bridge the gap between research breakthroughs and real-world applications. Claude is getting good at research. Given a well-posed problem, it can read the literature, write and run simulations, analyze results, and iterate on a design across physics, materials, chemistry, and engineering. But most of the hardest problems in science and engineering aren't well-posed. They live inside companies and labs with their own data, tools, constraints, and experts, and progress depends on turning all of that into work a model can actually do and check. Our Applied AI, Science & Engineering team takes Claude to the scientists and engineers working on hard problems in energy, materials, hardware design, and other physical-world fields. We build the tools Claude needs to do real research and engineering work. Mostly that means software: the agent harnesses, integrations, evaluations, and working processes that turn a partner's problem into something Claude can make progress on and validate. We find real-world problems and test what we build through partner engagements, then feed our learnings back to our research and product teams. It's early, and the engineers who join now will shape how Claude gets applied to science and engineering. This is an engineering role first. You'll spend real time with domain experts, become the person who knows how to get Claude working in their field, and prototype quickly alongside