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Gpu Compiler Engineer Jobs in Renton, WA (NOW HIRING)

Microsoft

Quantum Software Engineer II

Redmond, WA · On-site

$109.20K - $149.50K/yr

... AI tooling, GPU development, and more. This role is based in our Redmond office and requires a ... Building language and compiler features for Q#, OpenQASM, QIR, and related technologies that push ...

Microsoft

Senior Quantum Software Engineer

Redmond, WA · On-site

$137.20K - $180.90K/yr

... AI tooling, GPU development, and more. This role is based in our Redmond office and requires a ... Building language and compiler features for Q#, OpenQASM, QIR, and related technologies that push ...

Nvidia

Senior Performance Architect, Nemotron

Redmond, WA

$187K/yr

... development, compiler, and hardware teams to guide decisions that determine how efficiently ... Experience with GPU computing (CUDA) Your base salary will be determined based on your location ...

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Gpu Compiler Engineer information

See Renton, WA salary details

$12.4K

$93.7K

$127.1K

How much do gpu compiler engineer jobs pay per year?

As of May 30, 2026, the average yearly pay for gpu compiler engineer in Renton, WA is $93,688.00, according to ZipRecruiter salary data. Most workers in this role earn between $39,900.00 and $121,500.00 per year, depending on experience, location, and employer.

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

To thrive as a GPU Compiler Engineer, you need a strong background in computer science, with expertise in compiler design, parallel programming, and GPU architectures, typically supported by a relevant degree. Familiarity with tools and languages such as LLVM, CUDA, OpenCL, and performance profiling systems is essential. Analytical thinking, problem-solving ability, and effective collaboration are crucial soft skills for success in this role. These skills ensure the development of high-performance, reliable compiler solutions that optimize GPU-based applications and support innovation in computing.

What are some common challenges faced by GPU Compiler Engineers in optimizing code for different hardware architectures?

GPU Compiler Engineers often encounter challenges when adapting and optimizing code for a wide variety of GPU architectures. Each hardware platform may have unique instruction sets, memory hierarchies, and performance characteristics, requiring tailored compiler optimizations. Balancing performance, portability, and correctness is an ongoing challenge, especially when supporting multiple vendors or generations of hardware. Close collaboration with hardware engineers and performance analysts is essential to ensure the compiler produces efficient code that leverages the strengths of each GPU architecture.

What are GPU Compiler Engineers?

GPU Compiler Engineers are specialized software engineers who design, develop, and optimize compilers that translate high-level programming code into machine code that runs efficiently on Graphics Processing Units (GPUs). Their work enables developers to harness the full power of GPUs for tasks such as graphics rendering, scientific computing, and machine learning. These engineers often work closely with hardware teams to ensure compatibility and performance, and they play a critical role in advancing GPU technology.

What is the difference between Gpu Compiler Engineer vs Gpu Software Engineer?

AspectGpu Compiler EngineerGpu Software Engineer
Required CredentialsBachelor's or Master's in Computer Science, Electrical Engineering, or related; knowledge of compiler designBachelor's or Master's in Computer Science or related; strong programming skills
Work EnvironmentResearch and development teams focused on compiler optimization and hardware integrationSoftware development teams working on GPU applications, drivers, or SDKs
Industry UsagePrimarily in hardware and compiler companies, GPU manufacturers

The Gpu Compiler Engineer specializes in developing and optimizing compilers for GPU hardware, focusing on translating high-level code into efficient machine instructions. In contrast, the Gpu Software Engineer works on creating GPU-related software, such as drivers, SDKs, or applications. While both roles require strong programming skills and knowledge of GPU architecture, the compiler engineer emphasizes compiler design and optimization, whereas the software engineer focuses on software development and integration.

What job categories do people searching Gpu Compiler Engineer jobs in Renton, WA look for? The top searched job categories for Gpu Compiler Engineer jobs in Renton, WA are:
What cities near Renton, WA are hiring for Gpu Compiler Engineer jobs? Cities near Renton, WA with the most Gpu Compiler Engineer job openings:
Gpu Compiler Engineer jobs near you
Infographic showing various Gpu Compiler Engineer job openings in Renton, WA as of May 2026, with employment types broken down into 3% As Needed, 80% Full Time, 7% Temporary, 3% Contract, and 7% Nights. Highlights an 30% Physical, and 70% Hybrid job distribution, with an average salary of $93,688 per year, or $45 per hour.

Staff AI Software Engineer, Edge Model Optimization & Deployment

FieldAI

Seattle, WA

Full-time

Posted 26 days ago

Job description

FieldAI is transforming how robots interact with the real world. Our growing ML team in Seattle builds risk-aware, reliable, field-ready AI systems that tackle the hardest problems in robotics and unlock the potential of embodied intelligence. We take a pragmatic approach that goes beyond off-the-shelf, purely data-driven methods or transformer-only architectures, combining cutting-edge research with real-world deployment. Our solutions are already deployed globally, and we continuously improve model performance through rapid iteration driven by real field use.
 
We are seeking an accomplished Staff AI Software Engineer - Edge Model Optimization & Deployment to drive the optimization, integration, and deployment of our ML models on real robotic platforms. In this role, you will own the edge inference stack end to end, profiling and accelerating models, improving runtime performance across latency, throughput, memory, and power, and partnering closely with perception, autonomy, and platform teams to deliver robust on-robot behavior in the field. You will set technical direction, raise engineering rigor, and ensure our models run efficiently and reliably on constrained hardware across diverse environments.
 
This is an opportunity to shape the future of robotic autonomy by translating state-of-the-art ML into high-performance, production-grade edge deployments that operate reliably in complex, dynamic environments on real robots.
What You’ll Do:
  • Convert and optimize 2D/3D CNNs and Transformer-based models (PyTorch/TensorFlow → ONNX → TensorRT/Triton) for real-time inference on Jetson/Orin platforms.
  • Apply model compression techniques—quantization, pruning, distillation, weight sharing—to meet strict constraints on latency, memory, bandwidth, and power.
  • Develop custom TensorRT plugins and CUDA kernels for performance-critical components.
  • Integrate optimized models into the broader robotic system using ROS nodes and interfaces.
  • Build benchmarks, profile and debug end-to-end inference pipelines, and validate performance in real-world robotic scenarios.
  • Collaborate closely with AI researchers, robotics engineers, and hardware teams to translate cutting-edge research into robust, deployable edge solutions.
  • Ensure the reliability, robustness, and stability of deployed models operating continuously in challenging, resource-constrained environments.
What You Have:
  • 5+ years of professional experience developing and deploying deep learning models for edge, embedded, or real-time systems.
  • PhD in Computer Science, Robotics, Electrical or Computer Engineering, or a closely related technical field.
  • Strong proficiency in PyTorch, C++, Python, and CUDA for AI/ML development and model optimization.
  • Hands-on experience with TensorRT, ONNX, and Triton, including authoring custom plugins for TensorRT.
  • Proven experience applying model optimization techniques such as quantization, pruning, and distillation in production systems.
  • Deep understanding of hardware constraints and performance tuning on Jetson / ARM platforms, GPUs, and embedded Linux systems.
  • Experience integrating AI models into ROS-based robotic systems.
  • Ability to work independently while collaborating effectively in a fast-paced, cross-functional engineering environment.
The Extras That Set You Apart:
  • Experience with ROS2.
  • Experience writing and optimizing custom CUDA kernels and low-level GPU performance tuning.
  • Familiarity with Triton, ML compilers, or compiler-level optimizations for GPU inference.
  • Experience with JAX or additional ML frameworks beyond PyTorch.
  • Background deploying AI systems on real robots operating in the field, not just offline or in simulation.
  • Familiarity with NVIDIA’s edge and robotics ecosystem (e.g., Isaac ROS, DeepStream, JetPack).

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.