Machine Learning Engineer - PyTorch

Must Have: • 3+ years PyTorch operator or backend experience • Advanced C++17+ and template metaprogramming skills • Demonstrated work with PyTorch internals (ATen, dispatcher) • Prior custom op or extension authoring • Open • source PyTorch or TorchInductor contributions 1. Key Responsibilities • Design and implement new PyTorch operators and tensor functions in C++/ATen. • Build and validate Python bindings with correct gradient propagation and test coverage. • Create “golden” reference implementations in eager mode for correctness validation. • Collaborate asynchronously with CUDA or systems engineers who handle low-level kernel optimization. • Profile, benchmark, and report performance trends at the operator and graph level. • Document assumptions, APIs, and performance metrics for reproducibility. 1. Ideal Qualifications • Deep understanding of PyTorch internals (TensorIterator, dispatcher, autograd engine). • Strong background in C++17+ and template metaprogramming within PyTorch’s ecosystem. • Experience authoring or extending PyTorch custom ops or backends. • Working knowledge of performance profiling tools and GPU/CPU interplay. • Strong written communication and ability to deliver well-documented, self-contained modules. • Prior open-source contributions to PyTorch, TorchInductor, Triton, or related projects are a plus. 1. More About the Opportunity • Ideal for contractors who enjoy building clean, high-performance abstractions in deep learning frameworks. • Work is asynchronous, flexible, and outcome-oriented. • Collaborate with CUDA optimization specialists to integrate and validate kernels. • Projects may involve primitives used in state-of-the-art AI models and benchmarks. 1. Compensation & Contract Terms • Typical range: $100–$200/hour, depending on experience and project scope. • Structured as an independent contractor engagement, not employment. • Payments for services rendered on a milestone or weekly invoice cadence. • Confidentiality and IP assignment agreements may apply. 1. Application Process • Share a concise summary of your experience with PyTorch internals and systems-level programming. • Include links to open-source work, GitHub PRs, or sample operator implementations. • Provide hourly rate, availability, and relevant technical background. • Selected experts may complete a short, paid pilot module to demonstrate fit.