Research Engineer - RL Environments & Experiments

AI Chopping Block, Inc.; New York, Northern (NY, KY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Mecka AI is building the data infrastructure layer for robotics and embodied AI. This hands-on research engineering role focuses on constructing reinforcement learning environments, writing working software, and collaborating with product colleagues and domain experts to advance evaluation tools.

You will work with engineers to turn research prototypes into reusable environments, ensure experiments are reproducible, and communicate tradeoffs and findings clearly.

Qualifications

• Hands-on experience formulating RL tasks and constructing environments.
• Strong programming and debugging to build reusable research systems.
• Good experimental design and statistical reasoning with controlled comparisons.
• Ability to reason about environment dynamics and reward design.
• Independent research judgment and clear communication.

Responsibilities

• Build environments: define tasks, observations, actions and state transitions.
• Develop rewards and scoring: translate objectives into feedback and evaluation criteria.
• Run learning experiments: implement baseline agents, train and compare policies.
• Make evaluation reliable: separate training and held-out tasks, report uncertainty and performance.
• Investigate failures: inspect trajectories to identify root causes and inform next steps.
• Build with the team: validate assumptions with domain experts and turn prototypes into reusable tools.

Skills

RL environment expertise

Programming & debugging

Experimental design

Environment dynamics

Independent research judgment

Job description

Mecka AI is building the data infrastructure layer for robotics and embodied AI. This hands-on research engineering role focuses on constructing reinforcement learning environments, writing working software, and collaborating with product colleagues and domain experts to advance evaluation tools.

You will work with engineers to turn research prototypes into reusable environments, ensure experiments are reproducible, and communicate tradeoffs and findings clearly.