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Ml Compiler Engineer Jobs (NOW HIRING)

Neurophos Inc.

WI · On-site

We are seeking a talented ML Compiler Engineer to join our engineering team and lead the development of our compiler. This role focuses on compiler development for our novel LLM accelerator ...

Qualcomm

Machine Learning Compiler

Raleigh, NC · On-site

Responsibilities : • Drive innovation in ML compiler optimization algorithms, leveraging advanced mathematics, programming language theory, and modern compiler techniques. • Oversee feature ...

Neurophos Inc

Staff Compiler Engineer

Austin, TX · On-site

$240K - $280K/yr

We are seeking a talented ML Compiler Engineer to join our engineering team and lead the development of our compiler. This role focuses on compiler development for our novel LLM accelerator ...

Neurophos Inc.

We are seeking a talented ML Compiler Engineer to join our engineering team and lead the development of our compiler. This role focuses on compiler development for our novel LLM accelerator ...

NVIDIA Corporation

## Senior Machine Learning Applications and Compiler Engineer, LPXApplylocations: US, CA, Santa Clara ... Contributions to opensource ML frameworks, compilers, or runtime systems, particularly in areas ...

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 ...

TetraMem, Inc.

Compiler Engineer

San Jose, CA · On-site

$160K - $300K/yr

Collaborate closely with machine learning engineers to support model conversion, validation ... Knowledge of ML compilers such as MLIR * Experience with LLVM and other open-source compiler ...

Nvidia

Senior Performance Compiler Engineer - Triton

OR · On-site +1

$104K - $143K/yr

Contributions to open-source projects, especially in the AI/ML or compiler space. Familiarity with ... If you're a creative and autonomous engineer with a real passion for technology, we want to hear ...

Nutanix

Compiler Tools Engineer

Austin, TX · On-site

Job Area:Engineering Group, Engineering Group > Compiler Toolchain SoftwareGeneral Summary ... SummaryDevelop LLVM- and MLIR-based compiler components for Qualcomm's Hexagon DSPImprove ML ...

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How much do ml compiler engineer jobs pay per year?

As of Sep 13, 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 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.

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Ml Compiler Engineer jobs near you
Infographic showing various Ml Compiler Engineer job openings in the United States as of September 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $89,183 per year, or $42.9 per hour.

Model Bringup Engineer / ML Compiler Engineer

Manhattan, NY • On-site

General Compute Inc.
See more jobs at General Compute Inc.external

Other

Posted 12 days ago

Job description

Take a new model and get it running — correctly — on our ASIC in record time. When a frontier model drops, the only question that matters is how fast we can land it on our silicon and start serving it. You own that loop: from reference weights, through the compiler, to first correct tokens. The low-level runtime is co-owned with our hardware partner today; your job is everything it takes to get a brand-new architecture compiled, verified, and fast on top of it.

The bet of this role is that bringup should be an agentic loop, not a hand-port. You'll build the harness of agents that compiles, runs, diffs against reference, and localizes failures — so the marginal model comes up faster than the last one did. Correctness first, optimization second: get it right, prove it's right, then make it cheap. This is a senior IC role on a small team. You'll own the bringup pipeline, not tickets.

Responsibilities
  • Own model bringup end-to-end. Take a new architecture — a frontier LLM, an MoE, a multimodal model — from reference weights to first correct tokens running on our ASIC, in days, not quarters.
  • Build the agentic bringup loop. The differentiator isn't hand-porting one model — it's the harness of agents that compiles, runs, diffs against reference, localizes the failing op, and iterates without you in the inner loop. Each model you land should make the loop better at landing the next one.
  • Live in the compiler. Graph capture, IR lowering, op coverage, kernel selection — when a model won't compile or produces wrong numbers, the fix is yours, whether it's a missing lowering, a fused-kernel bug, or a numerics mismatch.
  • Own correctness before speed. Build the verification harness — layer-by-layer activation diffs, logit parity, end-to-end evals — that proves a freshly brought-up model matches reference before anyone trusts a token of it.
  • Then optimize. Once it's correct, make it fast: operator fusion, quantization, memory layout, batching and KV-cache behavior on our hardware. Bringup gets it running; this is where it earns its cost-per-token.
  • Work shoulder-to-shoulder with our hardware partner's compiler and runtime team. You're the person who turns 'the chip can technically run this' into 'this model is live and correct in production.'
What we're looking for
  • 5+ years in systems or ML systems, with real depth in at least one of: ML compilers, model porting/bringup, or high-performance kernels.
  • You've taken a model architecture you didn't design and made it run — and run correctly — on a target it wasn't written for. Numerics debugging doesn't scare you.
  • Strong on the internals of modern LLM inference: transformers, attention, KV cache, MoE routing, quantization, batching. You can read a new model's reference implementation and know what will be hard to lower.
  • Comfortable inside a compiler stack — MLIR/LLVM, XLA, or a vendor graph compiler — at the level of IR, lowering, and op coverage, not just calling into one.
  • Fluent with agentic tooling. You'd rather build the agent that runs the tedious bringup loop than run it by hand — and you have the taste to know where the loop still needs a human.
  • Self-directed. We don't assign tickets — you'll see the next model coming and have it half brought-up before anyone asks.
Nice to have
  • Have worked on a non-NVIDIA accelerator — TPU, Trainium/Inferentia, Tenstorrent, Groq, Cerebras, or similar — at the compiler or model-bringup layer.
  • Kernel-level experience in CUDA, Triton, or a vendor kernel language. You know why a fused attention kernel beats three unfused ops.
  • Have built eval and numerics-verification harnesses (logit parity, activation diffing) for models in production.
  • Contributed to a graph compiler or serving runtime — XLA, TVM, MLIR, vLLM, TGI, TensorRT-LLM, or SGLang.
  • Have built agent loops or LLM-driven tooling that did real engineering work, not demos.
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