1

Ml Compiler Engineer Jobs (NOW HIRING)

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 ...

Senior ML Compiler Engineer

San Bruno, CA

$124.80K - $171.40K/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 ...

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 ...

Senior ML Compiler Engineer

Austin, TX

$103.60K - $142.20K/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 ...

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

Senior ML Compiler Engineer

San Francisco, CA

$123.10K - $169.10K/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 ...

Senior ML Compiler Engineer

Warren, MI · On-site

$97.90K - $134.40K/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 ...

Senior ML Compiler Engineer

Sunnyvale, CA

$124.30K - $170.70K/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 ...

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

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

Senior ML Compiler Engineer

San Bruno, CA · On-site

$124.80K - $171.40K/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 ...

Senior ML Compiler Engineer

Sunnyvale, CA

$124.30K - $170.70K/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 ...

next page

Showing results 1-20

Ml Compiler Engineer information

See salary details

$33K

$89.2K

$142K

How much do ml compiler engineer jobs pay per year?

As of Jun 1, 2026, the average yearly pay for ml compiler engineer in the United States is $89,183.00, according to ZipRecruiter salary data. Most workers in this role earn between $66,500.00 and $109,000.00 per year, depending on experience, location, and employer.

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 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 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 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.

More about Ml Compiler Engineer jobs
What cities are hiring for Ml Compiler Engineer jobs? Cities with the most Ml Compiler Engineer job openings:
What states have the most Ml Compiler Engineer jobs? States with the most job openings for Ml Compiler Engineer jobs include:
What job categories do people searching Ml Compiler Engineer jobs look for? The top searched job categories for Ml Compiler Engineer jobs are:

ZK and ML Compiler Engineer

Polyhedra

San Francisco, CA

Other

Posted 24 days ago


Job description

ZK and ML Compiler Engineer

San Francisco Bay Area

We are at the forefront of Zero-Knowledge Machine Learning technology, developing breakthrough solutions that combine data-protective computation with advanced machine learning capabilities. Our compiler team plays a crucial role in making zkML practical and efficient.

We're seeking an exceptional Compiler Engineer with expertise in Zero-Knowledge proofs and cryptography to develop specialized compilers for ZK Machine Learning applications. This role sits at the intersection of cryptographic protocols, machine learning, and compiler optimization.

AI Tool Proficiency Requirements

  • Expert-level proficiency with advanced coding assistants (GitHub Copilot, Amazon CodeWhisperer, etc.)
  • Demonstrated ability to effectively prompt and interact with AI systems for maximum productivity
  • Strong experience using AI tools for debugging and optimization tasks
  • Experience integrating AI-assisted workflows into development processes
  • Ability to critically evaluate and validate AI-generated code and solutions

What You Will Do

  • Design and implement advanced compiler optimizations specifically for zkML circuits
  • Develop efficient arithmetic circuit representations for ML operations
  • Create and optimize intermediate representations for ZK proof systems
  • Implement novel proof-generation optimization techniques
  • Optimize constraint system generation and polynomial commitment schemes
  • Collaborate with cryptography and ML teams to implement efficient proving systems
  • Research and implement new optimization techniques for ZK-ML compilation
  • Contribute to the design of new ZK-friendly ML algorithms and architectures

Required Qualifications

  • Ph.D. or M.S. in Computer Science with focus on cryptography, compilers, or related field from a top-tier university
  • Strong background in Zero-Knowledge proofs and cryptographic protocols
  • Solid understanding of machine learning algorithms and their implementation
  • Expert-level proficiency in C++, Rust, or similar systems programming languages
  • Experience with ZK proof systems
  • Strong mathematical foundation in cryptography and abstract algebra

Preferred Qualifications

  • Experience with ML compiler optimization and frameworks
  • Contributions to ZK proof systems or compiler projects
  • Experience with proof system implementation
  • Publication record in relevant conferences (CCS, CRYPTO, PLDI, etc.)
  • Open-source contributions to ZK or compiler projects
  • ACM-ICPC Regional or World Finals medalist
  • USACO (USA Computing Olympiad) Gold/Platinum award
  • Top-tier algorithmic competition achievements

Technical Skills

  • Advanced knowledge of compiler design and implementation
  • Expertise in cryptographic primitives and protocols
  • Proficiency in optimization techniques for arithmetic circuits
  • Strong background in algorithm design and complexity theory
  • Experience with performance profiling and optimization
  • Familiarity with hardware architecture and constraints

What We Offer

  • Opportunity to work on cutting-edge zkML technology
  • Competitive compensation package
  • Professional development opportunities
  • Collaboration with leading researchers in ZK and ML
  • Impact on the future of data-protective computation