Staff Research Engineer

About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at The Role Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world’s leading AI labs and enterprises. We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: Environments for Software Engineering / coding agents UI-Environments for Computer-Use/Browser-Use agents MCP-based Environments for general function-calling agents across various enterprise and consumer applications. We are looking for a Staff Research Engineer to own the end-to-end lifecycle of RL environment projects, spanning environment design, task generation, reward/verifier design, quality, and delivery to frontier AI labs and enterprise clients. This is a hands-on technical leadership role where you influence revenue directly – you will be mapped to one or more AI labs and build RL environments specific to their needs. You will lead teams of engineers, subject matter experts (e.g. Finance expert, if you’re building an RL environment for investment banking workflows), researchers, and data ops teammates to achieve this. What You'll Do End-to-End Ownership: Lead RL Environment projects end-to-end for one or more clients, ensuring the environment you and your team create matches the client’s spec, surpasses quality expectations, and is delivered on time. Data Quality: Ensure the RL environments you produce, the data that goes into those environments, and the data generated from them (e.g. agent trajectories and reward scores) meet frontier standards for realism, difficulty, diversity. Team building and enablement: Work with your Ops counterparts to build the team of full-stack engineers, back-end engineers, domain experts, QAs, data creators, reviewers, and others you’ll need to deliver the environment on time. You’ll interview, hire, onboard, train, retain talent for your team Process Leadership: Set the process that each of the above team members follows to generate environment code, database schemas, seed data, tasks, and verifiers; set up quality rubrics, automated validation scripts, and human-in-the-loop review processes for every aspect of the environment and data for the environment. Customer Interaction: Own customer relationships for your RL Environment project(s), and act as the primary point of contact for leading AI labs, providing regular updates, asking for feedback, and identifying opportunities to grow project scope and revenue. Sales & Solutioning: participate in client solutioning conversations alongside our sales teammates; understand the needs of researchers at AI labs, translate those needs into environment goals Evals & Post-training: Demonstrate proof of value for your environments by running inhouse RL fine tuning experiments to measure model performance lifts on agent trajectories; or by producing eval reports of frontier models on your environment and tasks What We’re Looking For RL & Post-training experience: familiarity with RL fine tuning, verifier/reward design, and/or environment design Engineering Management experience: have led teams of engineers in the past, including interviewing/hiring them and setting up QA processes. Systems thinking + Database/API design: ability to ‘simulate’ the data schema and API interface of a consumer or business application Hands-on technical capability: willing to write code along with the team you’re managing; Python and SQL experience preferred Operational leadership: Proven ability to manage complex data pipelines, multi-stakeholder delivery, and concurrent high-stakes projects. Cross-functional communicator: ability to communicate clearly with researchers at frontier AI labs, subject matter experts for various domains, and diverse teams. Background in Computer Science, Machine Learning, or related technical field preferred. Why Turing Work directly with the world’s leading AI labs and enterprises at the cutting edge of RL environment design and post-training. Real impact: your environments will be used to evaluate and train frontier models on GDP-moving tasks across real-world domains Talent-dense team, where you'll find high autonomy, rapid iteration, and rapid learning curve This role is required to be in office five days a week, based in any of Turing's offices in San Francisco, Palo Alto, or Seattle. Compensation: $250,000 to $400,000 OTE + EquityValues We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI, helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise, applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation, with the speed, ownership, and impact of a startup. Turing is