Develop and implement CUDA Core Libraries in C++ and/or Python, including parallel algorithms and idiomatic language bindings for core CUDA functionality.
Compose, optimize, and evolve GPU algorithms and APIs, from high-level interfaces down to low-level performance tuning involving memory, parallelism, and synchronization.
Own features end-to-end: develop, implementation, testing, benchmarking, documentation, and long-term maintenance.
Improve developer experience across the stack: CI, tests, benchmarks, packaging, examples, and docs.
Collaborate with senior CUDA engineers in design reviews, code reviews, and open-source-style workflows.
Engage with real users through issues, performance investigations, and API feedback.

Requirements

BS, MS, or PhD in Computer Science, Computer Engineering, or a related field or equivalent experience.
Minimum of 8+ years of related development experience
Strong programming skills in C++, Python, or both, with proven interest in systems-level software (performance, memory, concurrency, API design).
Solid understanding of modern C++ (templates, generics, standard library) and/or Python library development and packaging.
Practical experience with parallel or heterogeneous programming (CUDA, OpenMP, GPU-accelerated Python, or similar).
Experience contributing to production software or open-source libraries, including testing, profiling, and code review.
Ability to work independently, scope problems, and drive projects to completion.
Clear written communication for technical design and documentation.
Comfort navigating large, multi-language codebases (C++, Python, CMake, Pixi, CI systems).

Benefits

equity
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Applicant Tracking System Keywords

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Hard Skills & Tools

C++PythonCUDAparallel algorithmsAPI designperformance tuningmemory managementconcurrencytestingprofiling

Soft Skills

independent workproblem scopingproject managementclear written communicationcollaboration

Certifications

BS in Computer ScienceMS in Computer SciencePhD in Computer Scienceequivalent experience