Machine Learning Researcher - RL and Agentic Systems
Tasks
- Automate dataset validation environment generation rollout analysis and benchmark construction
- Benchmark model behavior in reinforcement learning and agentic settings
- Build quality scorecards and evaluation methods
- Build scalable evaluation and validation tooling
- Collaborate with research and engineering teams to improve evaluation methodology and best practices
- Connect model failures to dataset environment and task design gaps
- Design and build datasets tasks and environments
- Develop frameworks for evaluating real world data quality
- Evaluate planning tool use robustness recovery and generalization
- Improve infrastructure for reproducible experimentation and evaluation quality
- Translate real world workflows into structured tasks interaction traces trajectories and stateful environments
Perks/Benefits
- N/A
Skills/Tech-stack
Agentic Systems | Benchmarking | Data Validation | Dataset Quality Evaluation | Dataset quality | Decision Making | Evaluation methodology | Experimental Design | Imitation Learning | Large Scale Data | Large-scale | Machine Learning | Offline Reinforcement Learning | Online Reinforcement Learning | Quality evaluation | RLAIF | RLHF | Reinforcement Learning | Reproducible experimentation | Reward Modeling | Sequential decision making | Simulation Environments | Task design
Education
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