Machine Learning Engineer — GPU Kernel
Institute of Foundation Models Open · verified Oct 22, 2026 On-site Sunnyvale Full-time Mid-level Machine Learning Engineering
At a glance
Mid-level Machine Learning Engineering role at Institute of Foundation Models. Sunnyvale · full-time · $150,000–$450,000 base.
SalaryStated by Institute of Foundation Models
$150,000 – $450,000
Base salary for this role, in USD per year, as published in the posting.
This role, from Institute of Foundation ModelsOur estimate for the market: $217,400–$302,625Median $259,100
Worked out from 8 postings that stated pay, median $259,100. Last updated Sep 29, 2026.
RoleMachine Learning Engineering
SeniorityMid-level
LocationSunnyvale
WorkplaceOn-site
EmploymentFull-time
Base pay$150,000–$450,000
PostedOct 5, 2026 · 2w ago
Role brief
aimlroles summary, based on the employer's posting.
What you'll do
- Profile deep-learning workloads on current hardware, then tune them to use compute and memory resources more efficiently
- Create benchmarks and tests that compare application performance across representative workloads
- Build automation for workload analysis and optimization, and investigate system bottlenecks affecting GPU use
- Develop kernels for new model architectures, review peers’ code, and verify CUDA outputs and gradients against references
What you bring
- Strong C++ skills and practical experience developing and tuning CUDA kernels for deep-learning workloads
- You understand GPU memory and execution behavior, and have used profiling results to guide optimization
- Strong Python skills plus experience connecting custom kernels to PyTorch or an equivalent framework
Who this fits
This mid-level role suits an engineer focused on CUDA kernel development and performance work for training and inference. You’ll work onsite in Sunnyvale.
From the employer
About the Institute of Foundation Models
We are a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy.
As part of our team, you’ll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub for high-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AI pioneers.
The Role
The GPU Kernel Engineer will play a role at the forefront of optimizing performance for the machine learning software stacks, especially at training and inference, and support the team to develop new and cutting-edge systems. The ideal candidate will have a strong background in parallel computing, and hands-on experience in system level coding, debug methodologies, and large-scale machine learning experience.
This role focuses on CUDA kernel development and optimization. Distributed training experience is a plus.
Key Responsibilities
- Understand, analyze, profile, optimize, and provide guidance to the team on deep learning workloads on state-of-the-art hardware and software platforms to improve their efficiency with different levels of optimization
- Design and implement performance benchmarks and testing methodologies to evaluate application performance
- Build tools to automate workload analysis, workload optimization, and other critical workflows
- Triage system issues and identify bottleneck and inefficiencies by analyzing the sources of issues and the impact on hardware, network and propose solutions to enhance GPU utilization
- Support the team to develop appropriate kernels and systems for new model architectures and algorithms
- Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
- Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- Represent MBZUAI at industry conferences and events, showcasing the institution’s cutting-edge HPC and deep learning capabilities and establishing MBZUAI as a global leader in AI research and innovation.
- Perform all other duties as reasonably directed by the line manager that are commensurate with these functional objectives.
- Validate CUDA kernel outputs and gradients against reference implementations, and benchmark representative shapes, dtypes, and model workloads.
Technical Qualifications
- Strong C++ skills and hands-on CUDA kernel development and optimization for deep-learning workloads.
- Understanding of GPU memory hierarchy, warp/block execution, and compute-memory trade-offs, with demonstrated profiling-driven optimization.
- Strong Python skills and experience integrating kernels with PyTorch or an equivalent framework, including numerical and gradient validation where needed.
- Experience with Triton, CUTLASS, or PTX/SASS analysis.
- Experience with multi-node distributed training or inference systems.
- Experience validating mixed-precision computations, such as BF16 or FP8.