Senior Machine Learning and Compiler Engineer, LPX

Senior Machine Learning Applications and Compiler Engineer page is loaded## Senior Machine Learning Applications and Compiler Engineer locations:
UK, Cambridge:
UK, Remote time type:
Full time posted on:
Posted Todayjob requisition :
JR2013261

NVIDIA is seeking engineers to develop algorithms and optimizations for our inference and compiler stack. You will work at the intersection of large-scale systems, compilers, and deep learning, crafting how neural network workloads map onto future NVIDIA platforms. This is your chance to be part of something outstandingly innovative!
** What you’ll be doing:
*** Build, develop, and maintain high-performance runtime and compiler components, focusing on end-to-end inference optimization.
* Define and implement mappings of large-scale inference workloads onto NVIDIA’s systems.
* Extend and integrate with NVIDIA’s SW ecosystem, contributing to libraries, tooling, and interfaces that enable seamless deployment of models across platforms.
* Benchmark, profile, and monitor key performance and efficiency metrics to ensure the compiler generates efficient mappings of neural network graphs to our inference hardware.
* Collaborate closely with hardware architects and design teams to feedback software observations, influence future architectures, and codesign features that unlock new performance and efficiency points.
* Prototype and evaluate new compilation and runtime techniques, including graph transformations, scheduling strategies, and memory/layout optimizations tailored to spatial processors.
* Publish and present technical work on novel compilation approaches for inference and related spatial accelerators at top tier ML, compiler, and computer architecture venues.
** What we need to see:
*** MS or PhD in Computer Science, Electrical/Computer Engineering, or related field, or equivalent experience, with 5 years of relevant experience.
* Strong software engineering background with proficiency in systems level programming (e.g., C/C++ and/or Rust) and solid CS fundamentals in data structures, algorithms, and concurrency.
* Hands on experience with compiler or runtime development, including IR design, optimization passes, or code generation.
* Experience with LLVM and/or MLIR, including building custom passes, dialects, or integrations.
* Familiarity with deep learning frameworks such as Tensor Flow and PyTorch, and experience working with portable graph formats such as ONNX.
* Solid understanding of parallel and heterogeneous compute architectures, such as GPUs, spatial accelerators, or other domain specific processors.
* Strong analytical and debugging skills, with experience using profiling, tracing, and benchmarking tools to drive performance improvements.
* Excellent communication and collaboration skills, with the ability to work across hardware, systems, and software teams.
* Ideal candidates will have direct experience with MLIR based compilers or other multilevel IR stacks, especially in the context of graph based deep learning workloads.
** Ways to stand out from the crowd:
*** Prior work on spatial or dataflow architectures, including static scheduling, pipeline parallelism, or tensor parallelism at scale.
* Contributions to opensource ML frameworks, compilers, or runtime systems, particularly in areas related to performance or scalability.
* Demonstrated research impact, such as publications or presentations at conferences like PLDI, CGO, ASPLOS, ISCA, MICRO, MLSys, NeurIPS, or similar.
* Experience with large-scale AI distributed inference or training systems, including performance modeling and capacity planning for multi rack deployments. #LI-Hybrid