Principal Research Engineer - RL Gyms

Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace  and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

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Turing's GDPR notice here

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Nice to have:

$250,000 to $350,000 OTE + Equity

Values:

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 looking for a

Frontier Data Lead – 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 you’re building an RL environment for investment banking workflows), researchers, and data ops teammates to achieve this.

Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world’s leading AI labs and enterprises, including OpenAI, Anthropic, Google DeepMind, Microsoft AI, Amazon, Apple, and many more. 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.