Machine Learning / AI Engineer (RL)

Technologies we use

About the project

Your responsibilities

• Design and implement RL environments that support large-scale agent evaluation and reinforcement learning experiments.
• Build task generation pipelines, dynamic datasets, and scripted environments with controlled complexity and stochasticity.
• Develop verifiers and reward models to automatically score trajectories and evaluate model reasoning.
• Collaborate with infrastructure and systems engineers to ensure environments are scalable, reproducible, and instrumented for detailed telemetry.
• Design APIs and orchestration frameworks for running, resetting, and evaluating agents across environments.
• Optimize environment performance, logging, and reward reproducibility across distributed setups.

Our requirements

• Experience as a Data Scientist, Machine Learning/Environment Engineer.
• Solid skills in Python software engineering.
• Being able to work 2 p.m. - 10 p.m. daily.
• Practical knowledge of AI frameworks (Langchain, Langraph, mcp-server ).
• Practice in working with AI, including prompt engineering and vibe coding.

Optional

• Knowledge of Codex or Claude Code.
• Experience in integrating AI with a system would be an advantage.
• Understanding of RL concepts - reward modeling, environment dynamics, verifiability, evaluation, and agent interaction loops.
• Familiarity with instrumentation, metrics, and data pipelines for RL evaluation.
• Expertise in planning your own work.

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Benefits