Research software engineer at Reflection who architects and optimizes large-scale reinforcement-learning training infrastructure — distributed GPU systems, training loops, and data pipelines — turning research ideas into reliable, reproducible production systems.
You will architect and optimize training infrastructure for reinforcement-learning loops, distributed GPU systems, and large-scale data pipelines. You will turn research ideas into reliable production systems, build experiment tooling, diagnose training bottlenecks, and improve numerical stability, performance, and reproducibility.
Responsibilities
Design and optimize large-scale training loops and data pipelines.
Implement state-of-the-art techniques with numerical stability and computational efficiency.
Build tooling to launch, monitor, and reproduce complex experiments.
Diagnose training-stack bottlenecks, including GPU memory, communication, and dataloader issues.
Translate research prototypes into reusable production-grade infrastructure.
Requirements
Software engineering skills and machine-learning knowledge.
Experience implementing research papers.
Deep experience in distributed training and inference or data infrastructure.
Knowledge of machine-learning algorithms, distributed systems, and high-performance computing.
Performance, numerical stability, and reproducibility expertise.
Benefits
Stock options.
Comprehensive medical, dental, vision, and life insurance.
Annual wellness allowance.
Daily in-office lunch and dinner.
22 weeks of paid parental leave.
Unlimited paid time off in the U.S. and 30 days in the U.K.
Visa sponsorship and long-term immigration-pathway support where applicable.
Regular off-sites, happy hours, and team celebrations.