Research Engineer (Reinforcement Learning)

Location: On-site in New York or San Francisco, United States

Salary: $170,000–$300,000 per year, plus potential equity

Company

hirify.global builds data infrastructure for robotics and embodied AI, including data capture, labeling, and hardware-enabled workflows for training and validating humanoid and embodied AI systems.

What you will do

• Build reinforcement learning environments by defining tasks, observations, actions, state transitions, reset behavior, termination conditions, and measurable outcomes.
• Develop reward functions, scoring systems, and evaluation criteria while testing whether agents exploit scoring rules.
• Run learning experiments by implementing baseline agents, training and comparing policies, and isolating the effects of data, methods, and environment changes.
• Make evaluations reliable through held-out tasks, leakage checks, experiment versioning, repeated runs, uncertainty reporting, and performance analysis.
• Investigate trajectories and learning behavior to identify whether failures originate from policies, data, rewards, or environments.
• Collaborate with domain experts, product colleagues, and engineers to turn research prototypes into reusable environments, evaluation tools, and documented methods.

Requirements

• Hands-on experience formulating reinforcement learning problems, constructing environments, training agents, and critically assessing results.
• Strong programming, software development, and debugging skills for research systems that others can run and extend.
• Sound experimental design and statistical reasoning, including controlled comparisons, evaluation splits, variability, and benchmark limitations.
• Ability to reason about environment dynamics, reward design, and agent behavior and trace unexpected results to concrete causes.
• Independent research judgment, clear communication, and the ability to learn unfamiliar domains and explain assumptions, tradeoffs, and findings.

Nice to have

• Experience building interactive environments, simulators, or benchmarks used by other researchers.
• Experience with agent evaluation, reward design, imitation learning, or learning from real-world data.
• Reproducible research artifacts with useful baselines and evidence of investigating failures beyond headline scores.

Culture & Benefits

• Hands-on research engineering work with frontier robotics teams and AI labs.
• Inclusive and supportive candidate experience with interview accommodations available.
• Equal-opportunity employment and a work environment committed to preventing discrimination, harassment, and retaliation.
• Compensation may include equity, health and wellness benefits, and other company programs.
• Recruitment decisions are made by people, with AI used only to support administrative and efficiency-focused processes.