Senior Machine Learning Applications and Compiler Engineer, LPX

NVIDIA Corporation; Cambridge; On-site; GBP 70,000 - 90,000; Full time

Benefits offered by this job

Flexible working hours

Job summary

A leading technology company in the UK is seeking a Senior Machine Learning Applications and Compiler Engineer to develop algorithms and optimizations for their compiler stack. You will focus on enhancing inference performance and collaborate closely with cross-functional teams. Qualified candidates will have a minimum of 5 years’ experience in software engineering, proficiency in C/C++, and familiarity with deep learning frameworks. This position offers the chance to work in a hybrid setting and contribute to innovative projects.

Qualifications

• 5 years of relevant experience in ML applications or compiler development.
• Strong software engineering background and solid CS fundamentals.
• Experience with machine learning model deployment.

Responsibilities

• Develop high-performance runtime and compiler components.
• Optimize large-scale inference workloads on NVIDIA’s systems.
• Collaborate with hardware architects and design teams.

Skills

C/C++ programming

Systems level programming

Compiler development

Deep learning frameworks

Benchmarking

Analytical skills

Education

MS or PhD in Computer Science or related field

Tools

LLVM

MLIR

TensorFlow

PyTorch

Job description

Senior Machine Learning Applications and Compiler Engineer page is loaded## Senior Machine Learning Applications and Compiler Engineerlocations: UK, Cambridge: UK, Remotetime type: Full timeposted on: Posted Todayjob requisition id: JR2013261NVIDIA 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 TensorFlow 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