Remote opportunity at
NVIDIASenior Machine Learning Applications and Compiler Engineer, LPX
NVIDIA is hiring a full-time Senior Machine Learning Applications and Compiler Engineer for a remote position. The selected professional will design algorithms and optimizations dedicated to…
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About This Role
NVIDIA is hiring a full-time Senior Machine Learning Applications and Compiler Engineer for a remote position. The selected professional will design algorithms and optimizations dedicated to the LPX compiler and inference stack. This role sits at the intersection of deep learning, compilers, and large-scale systems, shaping how neural network workloads map to upcoming NVIDIA platforms. The person in this position will create and maintain high-performance runtime and compiler elements centered…
Job Description
NVIDIA is hiring a full-time Senior Machine Learning Applications and Compiler Engineer for a remote position. The selected professional will design algorithms and optimizations dedicated to the LPX compiler and inference stack. This role sits at the intersection of deep learning, compilers, and large-scale systems, shaping how neural network workloads map to upcoming NVIDIA platforms.
The person in this position will create and maintain high-performance runtime and compiler elements centered on end-to-end inference optimization. Responsibilities include defining mappings for large-scale inference workloads onto systems, integrating with the software ecosystem, and profiling key performance metrics. Candidates will also partner with hardware architects to influence future designs, prototype compilation techniques, and share technical work at top venues.
This position requires an individual with a strong background in systems-level programming and compiler or runtime development. Ideal candidates bring experience with MLIR-based compilers, spatial architectures, or open-source contributions. NVIDIA offers a base salary range determined by location, experience, and objective factors, with equity and benefits provided.
Responsibilities
- 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 systems
- Extend and integrate with the software ecosystem by contributing to libraries, tooling, and interfaces
- Benchmark, profile, and monitor key performance and efficiency metrics for neural network graph mappings
- Collaborate with hardware architects to share observations, influence future architectures, and codesign features
- Prototype and evaluate new compilation and runtime techniques including graph transformations and memory optimizations
- Publish and present technical work on novel compilation approaches at top-tier venues
Requirements
- Five years of relevant experience along with an MS or PhD in Computer Science, Electrical or Computer Engineering, or a related field
- Proficiency in systems-level programming languages such as C, C++, or Rust
- Solid computer science fundamentals covering data structures, algorithms, and concurrency
- Hands-on experience with compiler or runtime development including IR design, optimization passes, or code generation
- Experience with LLVM or MLIR, including custom passes, dialects, or integrations
- Familiarity with deep learning frameworks like TensorFlow and PyTorch, and portable graph formats such as ONNX
- Understanding of parallel and heterogeneous compute architectures like GPUs and spatial accelerators
- Strong analytical, debugging, profiling, and benchmarking skills
- Communication and collaboration abilities to work across hardware, systems, and software teams
Qualifications
- Direct experience with MLIR-based compilers or multilevel IR stacks for graph-based deep learning workloads
- Prior work on spatial or dataflow architectures involving static scheduling, pipeline parallelism, or tensor parallelism
- Contributions to open-source machine learning frameworks, compilers, or runtime systems
- Demonstrated research impact through publications or presentations at conferences such as PLDI, CGO, ASPLOS, ISCA, MICRO, or NeurIPS
- Experience with large-scale AI distributed inference or training systems
Core Skills
Benefits
- Equity eligibility
- Standard benefits
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Is this position remote?
Yes, the posting lists the employment location as remote.
What is the salary for this role?
The expected base salary ranges from 135,000 CAD to 185,000 CAD for Level 3, and 170,000 CAD to 220,000 CAD for Level 4, determined by location, experience, and other objective factors. Equity and benefits are also included.
What are the core educational requirements?
Applicants need an MS or PhD in Computer Science, Electrical or Computer Engineering, or a related field, or equivalent experience, along with 5 years of relevant experience.
When is the application deadline?
Applications will be accepted at least until November 3, 2026.
Sample Interview Questions
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