Member of Technical Staff, Deeptune Environments

About Mercor

Mercor’s mission is to organize human intelligence for the AI economy. We build the infrastructure that enables frontier AI labs and enterprises to train, evaluate, and improve AI on real-world professional work. More than five million experts have joined our network, and we pay out over $5 million daily for their knowledge and expertise.

We built our training and evaluation infrastructure alongside the world’s leading AI labs. Now, we’re bringing those capabilities to enterprises across industries, enabling them to train, evaluate, and improve AI using their own expertise and standards. Our APEX benchmarks measure how AI performs on economically valuable work, while our enterprise evaluations and post-training capabilities turn those insights into measurable improvements across real-world workflows.

Our work sits at the intersection of frontier AI, human expertise, and real-world application. We’re solving problems few companies have encountered, with the opportunity to influence what the world’s most advanced AI systems become capable of doing. We’re looking for people who bring different perspectives, take ownership, exercise sound judgment, and hold themselves to an exceptionally high standard. You’ll have the opportunity to do consequential work alongside talented colleagues who challenge one another, collaborate openly, and share a commitment to excellence.

Mercor is profitable and recently raised a $750 million Series D at a $20 billion valuation. We work in person five days a week in San Francisco, New York, and London.

About Deeptune

Deeptune Environments is a Mercor team building the platform and tooling that AI labs use to train agents through reinforcement learning. We build the systems that let researchers turn real-world tasks into training environments at scale.

About the Role

You'll build the systems our high-fidelity simulation environments run on, the APIs and tool interfaces agents interact with, the grading layer that decides whether an agent actually succeeded, the pipelines that turn raw human demonstrations into training-ready environments, and the orchestration and sandboxing that run it all at scale.

You'll do this alongside researchers who own the RL side. Your job is to make their ideas real: fast, correct, and reliable at scale. That means you should know enough about post-training, evals, and reward modeling to push back on a research spec productively. It does not mean you'll be training models.

The work is high-ownership and lightly specified. You'll set direction, drive outcomes, and stay hands-on. You'll also work directly with AI labs and enterprise partners, which means shipping against real external deadlines rather than internal ones.

What You'll Do

• Build the systems that build environments end-to-end: the simulated app or system, the agent-facing tool surface, the task definitions, and the verifiers that score them.

• Design and operate the backend infrastructure that runs environments at scale (containers, orchestration, queues, observability) — standard distributed-systems backend work, applied to a new domain.

• Turn messy human data into clean, reproducible training environments.

• Care about reliability and speed as much as correctness.

• Own the interface with labs and researchers: translate a research goal into a system that exists next week.

What We're Looking For

We care more about what you've built than how long you've been building it.

• 2+ years of full-stack / backend engineering , including at least 1 year at a startup, ideally as a founding engineer, an early engineer at a fast-growing venture, or a founder yourself

• Strong generalist with systems depth . We primarily use Python and seek engineers who are fluent in at least one language. Ideally, they should be skilled at applying agents with discernment and able to quickly adapt to different problem requirements.

• Comfortable around ML/LLM concepts. Not an ML background, but enough working knowledge of post-training, evals, and reward modeling to partner with researchers and translate their specs into systems.

• Thrives in ambiguity . You scope your own work, make pragmatic calls, and ship without a spec handed to you.

• Ability to raise the bar around you . Coaching engineers and driving execution, while staying in the code

Bonus, not required: sandboxing or virtualization, browser and computer-use automation, CI/build systems, developer tooling.

You'll Fit Here If

• Ownership, impact, and building frontier tech are what motivate you

• Your work is a craft you want to master

• You thrive in ambiguity and like hard problems

• You appreciate diverse perspectives and uncommon ideas

• You're excited to build in person, 5 days a week, 10 am–8 pm ET, from our office at One World Trade

Mercor is an Equal Opportunity Employer and is committed to providing equal opportunity to all employees and applicants. Mercor does not discriminate based on actual or perceived race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth, related medical conditions and lactation), gender identity or gender expression, sexual orientation, marital status, military service and veteran status, disability, protected medical condition as defined by California or local law, genetic information, or any other characteristic protected by applicable federal, California, or local laws and ordinances. Further, Mercor prohibits discrimination, harassment, and retaliation based on any combination of protected characteristics. This Policy applies to all areas of employment, including recruitment, hiring, training, promotion, compensation, benefits, discipline, termination, and all other privileges, terms, and conditions of employment. This Policy and the law prohibit employment discrimination against any employee or applicant based on any legally protected status.