{"id":1930787,"url":"https://alion.io/job/institute-of-foundation-models-machine-learning-engineer-gpu-kernel","title":"Machine Learning Engineer — GPU Kernel","company":{"id":99421,"name":"Institute of Foundation Models","domain":"ifm.ai","url":"https://alion.io/company/institute-of-foundation-models","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":95,"open_postings":44,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":483,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sunnyvale, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":150000,"max":450000,"currency":"USD","period":"year","gross":null,"usd_annual":450000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Machine Learning","optional":false},{"name":"C++","optional":true},{"name":"CUTLASS","optional":true},{"name":"HPC","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"PyTorch C++","optional":true},{"name":"Sass","optional":true},{"name":"Triton","optional":true}],"status":"live","first_seen_at":"2026-10-05T21:20:01Z","employer_posted_date":"2026-10-05","last_verified_at":"2026-10-16T15:04:42Z","board_verified":true,"closed_at":null,"days_open":10,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":10},"description":"The Role\nThe 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.\nThis role focuses on CUDA kernel development and optimization. Distributed training experience is a plus.\nKey Responsibilities\nUnderstand, 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\nDesign and implement performance benchmarks and testing methodologies to evaluate application performance\nBuild tools to automate workload analysis, workload optimization, and other critical workflows\nTriage 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\nSupport the team to develop appropriate kernels and systems for new model architectures and algorithms\nParticipate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.\nReview code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).\nContribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.\nRepresent 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.\nPerform all other duties as reasonably directed by the line manager that are commensurate with these functional objectives.\nValidate CUDA kernel outputs and gradients against reference implementations, and benchmark representative shapes, dtypes, and model workloads.\nTechnical Qualifications\nMust-Haves:\nStrong C++ skills and hands-on CUDA kernel development and optimization for deep-learning workloads.\nUnderstanding of GPU memory hierarchy, warp/block execution, and compute-memory trade-offs, with demonstrated profiling-driven optimization.\nStrong Python skills and experience integrating kernels with PyTorch or an equivalent framework, including numerical and gradient validation where needed.\nNice-to-Haves:\nExperience with Triton, CUTLASS, or PTX/SASS analysis.\nExperience with multi-node distributed training or inference systems.\nExperience validating mixed-precision computations, such as BF16 or FP8.","description_format":"text","description_chars":2783,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Foundation Models","AI Research Labs"],"lifecycle":[{"event":"open","at":"2026-10-05T21:50:38Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring 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