Member of Technical Staff, Deeptune

Mercor•New York, NY

5d•$220,000 - $600,000•Onsite

About The Position

Deeptune is an RL environments lab within Mercor that builds training gyms for AI agents: high-fidelity simulations where AI learns to perform real-world tasks through reinforcement learning. We work with the leading AI labs to help them train the next generation of agentic models, and our environments have already contributed to recent breakthroughs in computer use, code generation, and multi-step task completion. This role requires high ownership and abstraction: setting direction, driving outcomes, and staying hands-on while leading. You'll also build direct partnerships with leading AI labs and enterprises, and contribute hands-on to improving frontier model quality through data, evaluation, and systems — work that sits squarely in the areas the field agrees matter most right now: RL with verifiable rewards, rubric-based reward modeling for subjective and agentic domains, and the pipelines that turn raw human demonstrations into training-ready environments.

Requirements

4 – 10 years in engineering
2–3+ years experience with Python, Go, TypeScript, etc.
Strong background in building scalable systems
Comfort with core ML/LLM concepts — post-training, evaluation metrics, reward modeling — deep enough to partner with researchers, not just consume their output
Ability to lead in ambiguous, fast-moving environments with sound technical judgment
Proven ability to coach engineers and drive execution
Strong ownership mindset with pragmatic decision-making
Deep interest in agentic AI
Motivated by ownership, impact, and building frontier tech.
Work is a craft you want to master.
Thrive in ambiguity and love hard problems.
Appreciate diverse perspectives and uncommon ideas.
Excited about building in-person, 5 days a week, from our office in One World Trade.

Nice To Haves

Been a founding engineer, founded your own company, or had a major impact at a top-tier company, and you’re ready to join an early-stage team.

Responsibilities

Set direction, drive outcomes, and stay hands-on while leading.
Build direct partnerships with leading AI labs and enterprises.
Contribute hands-on to improving frontier model quality through data, evaluation, and systems.
Work on RL with verifiable rewards.
Work on rubric-based reward modeling for subjective and agentic domains.
Work on pipelines that turn raw human demonstrations into training-ready environments.