{"id":2542864,"url":"https://alion.io/job/hiringcafe-ml-engineer-inference-model-deployment","title":"ML Engineer - Inference & Model Deployment","company":{"id":701954,"name":"HiringCafe","domain":"hiring.cafe","url":"https://alion.io/company/hiringcafe","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":true,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":7,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-15T05:46:00Z"}},"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":["Cupertino, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":250000,"max":310000,"currency":"USD","period":"year","gross":null,"usd_annual":310000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Fine-tuning","optional":false},{"name":"LLM","optional":false},{"name":"Quantization","optional":false},{"name":"SGLang","optional":false},{"name":"TensorRT","optional":false},{"name":"vLLM","optional":false}],"status":"live","first_seen_at":"2026-06-05T01:58:54Z","employer_posted_date":"2026-06-05","last_verified_at":"2026-10-15T11:12:39Z","board_verified":true,"closed_at":null,"days_open":132,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":132},"description":"Job discovery is broken. Indeed and LinkedIn want to keep it that way. Join our team and help millions of people put food on the table by finding the perfect job.\nHiringCafe.com is building a 100× better job search engine - fast, comprehensive, honest, and actually useful. We index millions of jobs, remove noise, rank what matters, and help people find real opportunities without dark patterns, ads, or pay-to-win placement.\nWe are looking for a founding ML engineer who can help us turn powerful AI and ML models into fast, reliable production systems. You will own the bridge between model development and real user-facing infrastructure: deploying models, optimizing inference latency and throughput, scaling serving systems, and making sure our models run efficiently in production.\nThis is a hands-on engineering role for someone who loves the details of model performance, GPU utilization, inference architecture, and production reliability.\nWhat You’ll Do\nDeploy and integrate researcher-trained model checkpoints into our cloud infrastructure and production pipelines.\n\nProfile and benchmark model performance to identify latency, throughput, memory, and compute bottlenecks.\n\nImplement optimization techniques such as quantization, pruning, batching, caching, efficient attention, and precision trade-offs while preserving model quality.\n\nBuild scalable multi-GPU inference systems for search, ranking, recommendations, agents, and other AI-powered product experiences.\n\nDesign reliable model-serving architecture that can support millions of users.\n\nDevelop efficient training and fine-tuning workflows where needed, including distributed training, mixed precision, and parallelism strategies.\n\nWork closely with our search & engineering teams to make model deployment a smooth part of our development workflow.\n\nYou May Be a Strong Fit If You\nHave deployed and optimized deep learning models in production environments.\n\nHave experience with large-scale model serving, multi-GPU inference, or high-throughput inference systems.\n\nUnderstand inference optimization techniques such as quantization, pruning, compilation, batching, caching, and memory optimization.\n\nHave strong instincts for profiling, benchmarking, and debugging model performance.\n\nAre familiar with efficient attention mechanisms, transformer optimization, or modern LLM/embedding/ranking model infrastructure.\n\nHave worked with inference frameworks or serving stacks such as SGLang, vLLM, TensorRT, or equivelant.\n\nCan write clean, production-quality code and integrate ML systems into backend infrastructure.\n\nAre comfortable with cloud platforms, distributed systems, storage systems, and modern ML training or serving workflows.\n\nWant ownership, leverage, and responsibility from day one.\n\nLogistics\nThis role is based in Cupertino, where we work in person. We believe the best ideas come from being in the same room.\nWe offer generous health, dental, and vision coverage, paid parental leave, and relocation support.\nDon’t meet every single qualification? That’s okay. We care more about your trajectory than checking every box. 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