Senior Data Scientist/AI Engineer (Reinforcement Learning)

Summary

Design and deploy reinforcement-learning environments and reward models to evaluate AI agents at scale, using Python and frameworks like Langchain.

Over 5 years of experience in software engineering in Python.
At least 3 years of experience in the position of Data Scientist, Machine Learning/Environment Engineering.
Working hours from 2:00 PM to 10:00 PM.
Practical knowledge of AI frameworks (Langchain, Langraph, mcp-server).
Extensive practical experience in working with artificial intelligence, including instant engineering and climate coding.

Knowledge of the Code of Conduct or Claude's Code.
Experience in integrating artificial intelligence with the system will be an additional asset.
Understanding of RL concepts - reward modeling, environmental dynamics, verifiability, evaluation, and agent interaction loops.
Knowledge of tools, metrics, and data channels for evaluating RL.
Expertise in planning own work.

Designing and deploying RL environments for large-scale agent evaluation and reinforcement learning experiments.
Create pipelines for task generation, dynamic datasets, and scripted environments with controlled complexity and stochasticity.
Develop validators and reward models to automatically evaluate trajectories and assess model inference.
Collaborate with infrastructure and systems engineers to ensure scalability, reproducibility, and equip environments with tools for detailed telemetry.
Design API interfaces and orchestration structures for running, resetting, and evaluating agents in various environments.
Optimization of environment performance, reward logging, and reproducibility in distributed configurations.