Responsibilities
- Build evaluation benchmarks and metrics
- Build and iterate on agent harness, including context engineering, agent memory, tools, skills
- Develop robust, reproducible training workflows from data ingestion and preprocessing through model checkpointing and deployment
- Design RL environments and reward functions
- Debug and optimize training runs
- Conduct training runs to improve model capabilities for agentic applications
Requirements
- BS in CS, EE, Math or related STEM field
- 5+ years software development background
- 2+ years of hands‑on experience in machine learning engineering, data science or ML research
- Proficient in Python
- Proficient in LLM architectures, optimization and model training dynamics
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