Strong background in deep learning or reinforcement learning
Expert-level Python programming skills
Experience with ML frameworks like PyTorch, JAX, TensorFlow
Knowledge of distributed computing and large-scale ML pipelines
Curiosity and hands-on experimentation skills
Expertise with advanced AI tools to enhance productivity and code quality
Responsibilities
Prototype and scale experimental models on large data
Build tools and pipelines for training and evaluation
Implement and iterate on state-of-the-art research
Own the full machine learning lifecycle from data to deployment
Provide engineering expertise in an autonomous environment
Benefits
Opportunity to work on AI with real stakes
Join an early team with repeat founders
Enjoy a high-agency, low-bureaucracy work environment
Full Job Description
About the Role
You'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.
