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Ml Compiler Engineer Jobs in California (NOW HIRING)

femtoAI

ML Compiler Engineer

San Bruno, CA · On-site

You will work on a custom ML compiler that transforms modern ML and DSP models into highly efficient programs for our accelerator. This role spans the full compiler stack-from ingesting models and ...

femtoAI

Senior ML Compiler Engineer

San Bruno, CA · On-site

$124K - $171K/yr

Masters or PhD in Computer Science, Electrical Engineering, Math, or a related field * Deep experience with at least one: * ML compiler frameworks (MLIR, TVM, XLA, etc.) * Low-level optimization ...

General Motors

Senior ML Compiler Engineer

Sunnyvale, CA · On-site

$122K - $168K/yr

The Role As a Senior Compiler Engineer on the AI Kernels & Compilers team, you will work on the ... Experience with ML frameworks (e.g., PyTorch, TensorFlow, JAX) and software stack (e.g., ONNX, MLIR ...

General Motors

Senior ML Compiler Engineer

Sunnyvale, CA · On-site

$122K - $168K/yr

If you want your compiler andkernelsworktodirectlyinfluencehow automated vehicles understand and ... GPU programming (CUDA) and familiarity with ML SW stack (e.g.,cuDNN,cuBLAS) * Experience with ML ...

SF Tensor

Founding GPU Compiler Engineer

San Francisco, CA · On-site

$163K - $200K/yr

PTX/SASS, GCN/RDNA assembly, or other GPU ISAs • Familiarity with ML compiler stacks (XLA, TVM, Triton, torch.compile, or similar) • Solid systems programming skills in C++ and/or Rust • Proven ...

DensityAI

Compiler Engineer -- MLIR

Mountain View, CA · On-site

... ML frameworks to silicon. • Use and develop AI-assisted tool flows to accelerate compiler ... compiler engineering experience, with hands-on MLIR contributions or equivalent IR-design ...

Efficient Computer

Senior Compiler Engineer

San Jose, CA · On-site

$160K - $210K/yr

This level of efficiency makes perpetual, pervasive intelligence possible: run AI/ML continuously ... The Compiler Engineer will contribute to the design and implementation of an embedded machine ...

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Ml Compiler Engineer information

What does an ML Compiler Engineer do?

An ML Compiler Engineer designs and develops compilers and software tools that optimize machine learning models for deployment on various hardware platforms. Their work involves translating high-level ML code into optimized, low-level instructions that can run efficiently on CPUs, GPUs, or specialized accelerators. They collaborate closely with hardware engineers and ML researchers to ensure models execute quickly and accurately. Additionally, ML Compiler Engineers may work on improving performance, reducing memory usage, and supporting new ML frameworks or hardware.

What are some typical collaboration points between an ML Compiler Engineer and other teams during a project?

ML Compiler Engineers frequently collaborate with machine learning researchers to understand model requirements, with hardware engineers to optimize for specific accelerators, and with software developers to ensure seamless integration into production systems. This role often involves participating in cross-functional meetings, code reviews, and design discussions to align compiler optimizations with both hardware capabilities and end-user needs. Effective communication and teamwork are essential, as these engineers play a central role in bridging the gap between algorithm design and efficient execution on target platforms.

What are the key skills and qualifications needed to thrive as an ML Compiler Engineer, and why are they important?

To thrive as an ML Compiler Engineer, you need a strong background in computer science, compiler design, machine learning concepts, and typically a degree in computer science or a related field. Familiarity with tools like LLVM, MLIR, TensorFlow XLA, and programming in C++ and Python is often required, along with experience in optimizing machine learning workloads. Strong problem-solving abilities, attention to detail, and effective collaboration skills help set top professionals apart. These skills ensure efficient translation and optimization of ML models for diverse hardware, enhancing performance and scalability.

What is the difference between Ml Compiler Engineer vs Machine Learning Engineer?

AspectMl Compiler EngineerMachine Learning Engineer
Required SkillsProgramming, compiler design, optimization, ML frameworksData analysis, model development, programming, ML frameworks
Work EnvironmentResearch labs, tech companies, AI hardware firmsTech companies, startups, data-driven organizations
CertificationsComputer science, software engineering, specialized compiler coursesMachine learning, data science, AI certifications
Industry UsageAI hardware, software optimization, ML infrastructureModel development, deployment, data analysis

While both roles involve machine learning, Ml Compiler Engineers focus on optimizing ML models through compiler design and software performance, whereas Machine Learning Engineers develop and deploy ML models for applications. The roles often overlap in skills but differ in their primary focus areas.

What are popular job titles related to Ml Compiler Engineer jobs in California?

For Ml Compiler Engineer jobs in California, the most frequently searched job titles are:

What job categories do people searching Ml Compiler Engineer jobs in California look for?

The top searched job categories for Ml Compiler Engineer jobs in California are:

What cities in California are hiring for Ml Compiler Engineer jobs?

Cities in California with the most Ml Compiler Engineer job openings:

Ml Compiler Engineer jobs near you
Infographic showing various Ml Compiler Engineer job openings in California as of August 2026, with employment types broken down into 95% Full Time, 2% Part Time, and 3% Contract. Highlights an 85% Physical, 6% Hybrid, and 9% Remote job distribution.

ML Compiler Engineer

femtoAI

San Bruno, CA • On-site

Full-time

Medical, Dental, Vision, Retirement

Re-posted 10 days ago

Job description


You will work on a custom ML compiler that transforms modern ML and DSP models into highly efficient programs for our accelerator.
This role spans the full compiler stack-from ingesting models and transforming intermediate representations to optimizing execution under tight memory and latency constraints.
What You'll Do
  • Build and maintain model ingestion pipelines (e.g., PyTorch / ONNX → internal IR)
  • Implement graph transformations such as:
    • Operator decomposition and canonicalization
    • Shape inference and layout transformations
  • Develop and extend intermediate representations (e.g., MLIR)
  • Implement optimization passes including:
    • Operator fusion and graph partitioning
    • Basic scheduling and tiling strategies
    • Memory planning and reuse
  • Debug correctness and numerical issues across transformations
  • Collaborate with hardware and ML teams to improve system performance

Requirements
Required qualifications and experience:
  • 2+ years of experience in compilers and/or edge-AI
  • Proficiency in Python and/or C++
  • Experience with at least one:
  • MLIR, LLVM, TVM, XLA, or similar
  • Graph-level transformations or ML model internals
  • Understanding of deep learning models (conv, sequence models, etc.)
  • Ability to reason about correctness and performance tradeoffs

Nice to have:
  • Experience with optimization techniques (tiling, scheduling, memory reuse)
  • Familiarity with ONNX or PyTorch internals
  • Exposure to quantization or low-precision computation
  • Interest in hardware-aware ML systems

Benefits
  • 401(k)
  • Medical insurance
  • Vision insurance
  • Dental insurance
  • Commuter benefits
  • Disability insurance
  • Paid maternity leave
  • Paid paternity leave
  • Child care support

femtoAI is an equal opportunity employer committed to a diverse workforce which strives to create an inclusive working environment empowering everyone to do their best work. We do not discriminate on the basis of race, ethnicity, religion, gender, gender identity, sexual orientation, age, marital status, veteran status, or disability status.

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