Own problems end-to-end from exploratory R&D through production-hardened, maintainable systems
Develop and extend GPU-accelerated code in PyTorch and CUDA C++
Work across a broad modeling landscape including RL, graph neural networks, black-box/classical optimization, and generative modeling
Formulate objectives, model constraints, and debug numerical behavior in the stack
Contribute to technical direction and research strategy alongside senior teammates

Requirements

4+ years of industry experience in ML, optimization, or a related field
Strong fundamentals in machine learning and optimization
Production PyTorch experience
Demonstrated ability to work across research and production codebases
Comfort operating with high autonomy in ambiguous problem spaces
Strong communication and collaboration skills
Preference: 5–7 years of industry experience, CUDA C++ experience, background in reinforcement learning, geometric deep learning, graph neural networks, multi-objective optimization, combinatorial optimization

Benefits

Competitive salary and equity benefits
Health, dental, and vision insurance
Regular team events and offsites (~4x / year)
Unlimited paid time off
Paid parental leave

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

GPU-accelerated codePyTorchCUDA C++machine learningoptimizationreinforcement learninggraph neural networksgenerative modelingblack-box optimizationclassical optimization

Soft Skills

strong communicationcollaborationautonomyproblem-solving