Principal Software Engineer
Architects high-performance infrastructure for training large-scale neuroscience-informed AI models and RL environments. Optimizes code, resolves critical bottlenecks, and scales research prototypes to production systems.
About the job
Responsibilities
- Define and architect high-performance infrastructure for training large-scale models and running complex simulations.
- Design and optimize custom RL environments informed by neuroscience.
- Set the organizational standard for clean, fast, production-grade code that boosts research velocity.
- Turn research prototypes into scalable production systems.
- Identify and resolve the most critical and ambiguous technical bottlenecks, from low-level optimization to high-level strategy, to unblock the research team.
Qualifications
- High-Velocity Craftsmanship: Set the bar for clean, maintainable, and well-tested code at a startup pace. Balance engineering rigor with quick iteration on research prototypes.
- System Architecture Intuition: Proven success architecting complex systems from scratch. Understand multi-year trade-offs and design infrastructure that survives research chaos.
- Extreme Adaptability: Expert in distributed systems today, ready to master neuroscience tomorrow. Track record of diving into complex domains and guiding others.
- Mastery of Hard Problems: Track record leading resolution of difficult engineering challenges, such as massive scale distributed systems or kernel-level optimization.
Skills
Python, Distributed Systems, Reinforcement Learning, System Architecture, High-Performance Computing, Large-Scale Models, Rl Environments, Production Systems, Low-Level Optimization, Scalable Infrastructure
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