Staff Research Engineer RL Gyms

Based in San Francisco California Turing is the worlds leading research accelerator for frontier AI labs and a trusted partner for global enterprises looking to deploy advanced AI systems. Turing accelerates frontier research with high-quality data specialized talent and training pipelines that advance thinking reasoning coding multimodality and STEM. For enterprises Turing builds proprietary intelligence systems that integrate AI into mission-critical workflows unlock transformative outcomes and drive lasting competitive advantage.

Recognized by Forbes The Information and Fast Company among the worlds top innovators Turings leadership team includes AI technologists from Meta Google Microsoft Apple Amazon McKinsey Bain Stanford Caltech and MIT. Learn more at

Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the worlds 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:

1. Environments for Software Engineering / coding agents
2. UI-Environments for Computer-Use/Browser-Use agents
3. MCP-based Environments for general function-calling agents across various enterprise and consumer applications.

We are looking for a Staff Research Engineer RL 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 youre building an RL environment for investment banking workflows) researchers and data ops teammates to achieve this.

• 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 clients 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 youll need to deliver the environment on time. Youll 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

• 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 youre 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.

• Work directly with the worlds 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 youll find high autonomy rapid iteration and rapid learning curve

• $250000 to $350000 OTE Equity

• 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.