Builds high-performance infrastructure for brain reverse-engineering via large-scale AI models and neuroscience-informed simulations. Architects systems, optimizes RL environments, and scales research prototypes to production.
100k – 400k/yr
HybridML Engineering
About the role
Responsibilities
Architect high-performance infrastructure for training large-scale models and running complex simulations.
Design and optimize custom RL environments informed by neuroscience.
Write clean, fast, production-grade code that boosts research velocity.
Turn research prototypes into scalable production systems.
Tackle technical bottlenecks from low-level optimization to high-level system design.
Qualifications
Produce clean, maintainable, well-tested code at startup pace, balancing rigor with quick iteration.
Deep experience architecting complex systems from scratch, understanding trade-offs.
Extreme adaptability to dive into new domains like neuroscience.
Track record tackling difficult engineering challenges like massive scale distributed systems or low-level optimization.
Skills
PythonReinforcement LearningDistributed SystemsHigh-Performance ComputingSystem ArchitectureLow-Level OptimizationScalable Production SystemsRl Environments
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