About the RoleYou'll bridge research and engineering-rapidly implementing, experimenting with, and scaling new algorithms and models. You'll work closely with scientists and founders to translate ideas into high-performance systems, and will operate across the stack from prototyping to deployment.
Responsibilities- Prototype and scale experimental models (LLMs, RL agents, agentic systems) on large, real-world data.
- Build tools and pipelines for training, evaluation, and analysis.
- Implement state-of-the-art research from papers and iterate in collaboration with scientists.
- Own the full ML lifecycle: data engineering, experimentation, training, and deployment.
- Operate in a highly autonomous, engineering-driven environment.
Requirements- Strong background in deep learning, reinforcement learning, or computational modeling.
- Expert-level Python and significant experience in ML frameworks (PyTorch, JAX, TensorFlow).
- Ability to translate research into robust, scalable systems.
- Experience with distributed computing or large-scale ML pipelines.
- Demonstrated curiosity and hands-on experimentation skills.
- Expertise in leveraging the latest AI tools (Cursor, Claude Code, Codex, etc) to increase productivity & code output while maintaining high code quality, maintainability, and structure.
