Senior Machine Learning Applications and Compiler Engineer, LPX
At a glance
Senior Machine Learning Engineering role at NVIDIA. US, CA, Santa Clara · full-time.
In listing Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Develop runtime and compiler components that raise inference performance across the full workload
- Decide how large inference workloads run on NVIDIA systems, then extend the libraries, tools and interfaces around them
- Benchmark and profile neural-network graph mappings, tracking compiler efficiency on inference hardware
- Evaluate graph changes, scheduling approaches and memory layouts for spatial processors
- Share software performance findings with hardware teams and present new inference compilation work at technical venues
What you bring
- A relevant master's or doctoral degree, or equivalent experience, alongside at least five years of related work
- Systems programming skills in C/C++ or Rust, plus hands-on compiler or runtime work involving IRs, optimization or code generation
- Experience building LLVM or MLIR integrations, including custom compiler passes or dialects
- Familiarity with TensorFlow, PyTorch and portable graph formats such as ONNX
- Knowledge of parallel and heterogeneous processors, with profiling, tracing or benchmarking experience to improve performance
Who this fits
This senior role suits an engineer who works across compiler, runtime and inference performance, and can collaborate with hardware and software teams. You'll combine implementation and measurement with testing new techniques and presenting technical results.
From the employer
We are now looking for a Senior Machine Learning Applications and Compiler Engineer!
NVIDIA is seeking engineers to develop algorithms and optimizations for our LPX 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.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until July 17, 2026.This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.