{"id":829154,"url":"https://alion.io/job/river-ai-software-engineer-distributed-training","title":"Software Engineer, Distributed Training","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":"2026-10-31T05:58:55Z"}},"role":"Backend","role_family":"Backend","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":"Fine-tuning","optional":false},{"name":"JAX","optional":false},{"name":"Mixture of Experts","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"C++","optional":true},{"name":"CUDA","optional":true},{"name":"LoRA","optional":true},{"name":"NCCL","optional":true},{"name":"PEFT","optional":true},{"name":"Pre-training","optional":true},{"name":"Rust","optional":true}],"status":"live","first_seen_at":"2026-09-12T02:03:30Z","employer_posted_date":"2026-09-20","last_verified_at":"2026-10-31T06:57:43Z","board_verified":true,"closed_at":null,"days_open":49,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":49},"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 systems engineers to build the distributed training engines behind the River API. Your goal is to make fine-tuning and reinforcement learning fast, numerically correct, and reliable across large GPU clusters.\nYou will own the execution of training workloads, including gradient computation, optimizer updates, rollout coordination, and checkpoint recovery. Working closely with researchers and inference engineers, you will bring new learning methods into production and improve how efficiently models use compute.\nWhat You’ll Do\nBuild and optimize distributed training for large dense and mixture-of-experts models, including low-rank adapter training.\nImprove reinforcement-learning pipelines by coordinating sampling, reward computation, training updates, and weight transfer.\nOptimize GPU memory use, parallelism, and communication to increase training throughput.\nImplement reliable checkpointing, resumption, and worker recovery while preserving consistent training state.\nValidate losses, gradients, and optimizer behavior, and diagnose numerical or distributed execution failures.\nPartner with researchers to implement new algorithms and make them accessible through the River API.\nSkills & Qualifications\nMinimum Qualifications:\nBachelor’s degree in Computer Science, Computer Engineering, or equivalent practical industry experience.\nHands-on experience building or substantially improving distributed model-training systems.\nStrong proficiency in Python and a modern deep-learning framework, such as PyTorch or JAX.\nSolid understanding of backpropagation, optimizers, mixed-precision training, and GPU memory management.\nStrong debugging skills across concurrent execution, collective communication, and distributed failure recovery.\nA highly collaborative mindset and a bias for action to push boundaries across the stack.\nPreferred Qualifications: (We encourage you to apply even if you don't meet all of these)\nExperience with reinforcement-learning infrastructure, rollout generation, or asynchronous training.\nFamiliarity with tensor, pipeline, expert, or data parallelism and their performance tradeoffs.\nWork on LoRA, mixture-of-experts training, distributed optimizers, or activation checkpointing.\nExperience with NCCL, communication profiling, and overlapping computation with data transfers.\nProficiency in C++, Rust, or CUDA, with experience investigating performance below the framework layer.\nContributions to training frameworks or a track record of operating large training runs.\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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