{"id":829153,"url":"https://alion.io/job/river-ai-software-engineer-gpu-kernels","title":"Software Engineer, GPU Kernels","company":{"id":25477,"name":"River AI","domain":"river.ai","url":"https://alion.io/company/river-ai","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"C","score":68,"open_postings":19,"ghost_share":0.526,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-28T05:59:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Palo Alto, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":200000,"max":420000,"currency":"USD","period":"year","gross":null,"usd_annual":420000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Accelerate","optional":false},{"name":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"CUTLASS","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Mixture of Experts","optional":false},{"name":"Python","optional":false},{"name":"Triton","optional":false},{"name":"Pre-training","optional":true},{"name":"PyTorch","optional":true},{"name":"SGLang","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-09-12T02:05:23Z","employer_posted_date":"2026-09-20","last_verified_at":"2026-10-28T23:25:42Z","board_verified":true,"closed_at":null,"days_open":46,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":46},"description":"At River AI, our mission is to create personal AI owned and shaped by each individual. To achieve this, we are rewriting the entire stack from scratch: personal hardware for local inference, bespoke training infrastructure, next-generation UIs, and frontier deep learning research.\nWho we are\nWe are scientists, engineers, and builders from the industry's top tech companies and AI labs. We bring a proven track record of scaling consumer systems for hundreds of millions of users and architecting the pre-training infrastructure behind today's frontier models.\nAbout the Role\nWe are looking for exceptional GPU kernel engineers to build the compute primitives behind River’s training and inference infrastructure. Your goal is to make large models faster to train and more efficient to serve.\nYou will own performance-critical operations, including attention, matrix multiplication, mixture-of-experts execution, and low-precision computation. Working closely with researchers and systems engineers, you will identify bottlenecks, implement kernels, validate correctness, and bring improvements into production.\nWhat You’ll Do\nBuild fast GPU kernels for attention, matrix multiplication, expert routing, and related operations.\nOptimize memory access, tiling, and synchronization to make efficient use of GPU hardware.\nDevelop FP8, FP4, and mixed-precision kernels while preserving numerical correctness.\nAccelerate fine-tuning and RL through optimized adapters, backward passes, and fused operations.\nProfile real workloads and integrate improvements into training and inference runtimes.\nBuild reproducible benchmarks that verify correctness, gradients, and performance.\nSkills & Qualifications\nMinimum Qualifications:\nBachelor’s degree in Computer Science, Computer Engineering, or equivalent practical experience.\nExperience optimizing GPU kernels with CUDA, Triton, CUTLASS, CuTe, or comparable tools.\nStrong understanding of GPU architecture, memory hierarchies, and parallel execution.\nProficiency in C++ and Python.\nStrong foundations in linear algebra, floating-point arithmetic, and numerical computing.\nStrong debugging and profiling skills, with a collaborative approach to engineering.\nPreferred Qualifications: (We encourage you to apply even if you don't meet all of these)\nExperience optimizing for NVIDIA Blackwell or Hopper GPUs.\nWork on attention, mixture-of-experts kernels, grouped GEMMs, or low-rank adapters.\nExperience implementing backward passes and validating gradients.\nFamiliarity with FP8, FP4, and quantized weight layouts.\nExperience integrating custom operators into PyTorch, SGLang, vLLM, or similar frameworks.\nOpen-source contributions or a track record of shipping substantial kernel optimizations.\nLogistics & Benefits\nLocation: Palo Alto, California.\nCompensation: Depending on experience and skills the expected base pay is $200,000 - $420,000 USD per year, plus equity.\nBenefits: Comprehensive health, dental, and vision insurance; 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