{"id":3425491,"url":"https://alion.io/job/featherless-ai-ai-researcher-inference-optimization","title":"AI Researcher — Inference Optimization","company":{"id":4483944,"name":"Featherless AI","domain":null,"url":"https://alion.io/company/featherless-ai","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"worldwide","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"KV Cache","optional":false},{"name":"Machine Learning","optional":false},{"name":"ONNX Runtime","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"TensorRT","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false},{"name":"CUDA","optional":true},{"name":"ROCm","optional":true},{"name":"Speculative Decoding","optional":true}],"status":"live","first_seen_at":"2026-09-25T02:49:40Z","employer_posted_date":null,"last_verified_at":"2026-09-25T02:49:40Z","board_verified":false,"closed_at":null,"days_open":33,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":33},"description":"Role OverviewWe are seeking an AI Researcher with deep experience in inference optimization to design, evaluate, and deploy high-performance inference systems for large-scale machine learning models. You will work at the intersection of model architecture, systems engineering, and hardware-aware optimization, improving latency, throughput, and cost efficiency across real-world production environments.\nKey Responsibilities\nResearch and develop techniques to optimize inference performance for large neural networks.\n\nImprove latency, throughput, memory efficiency, and cost per inference.\n\nDesign and evaluate model-level optimizations (quantization, pruning, KV-cache optimization, architecture-aware simplifications).\n\nImplement systems-level optimizations (dynamic batching, kernel fusion, multi-GPU inference, prefill vs decode optimization).\n\nBenchmark inference workloads across hardware accelerators.\n\nCollaborate with engineering teams to deploy optimized inference pipelines.\n\nTranslate research insights into production-ready improvements.\n\nRequired Qualifications\nStrong background in machine learning, deep learning, or AI systems.\n\nHands-on experience optimizing inference for large-scale models.\n\nProficiency in Python and modern ML frameworks (e.g., PyTorch).\n\nExperience with inference tooling (e.g., Triton, TensorRT, vLLM, ONNX Runtime).\n\nAbility to design experiments and communicate results clearly.\n\nPreferred / Nice-to-Have Qualifications\nExperience deploying production inference systems at scale.\n\nFamiliarity with distributed and multi-GPU inference.\n\nExperience contributing to open-source ML or inference frameworks.\n\nAuthorship or co-authorship of peer-reviewed research papers in machine learning, systems, or related fields.\n\nExperience working close to hardware (CUDA, ROCm, profiling tools).\n\nWhat Success Looks Like\nMeasurable gains in latency, throughput, and cost efficiency.\n\nOptimized inference systems running reliably in production.\n\nResearch ideas successfully translated into deployable systems.\n\nClear benchmarks and documentation that inform product decisions.\n\nRelevant Research Areas (Bonus)\nLong-context inference optimization\n\nSpeculative decoding\n\nKV-cache compression and paging\n\nEfficient decoding strategies\n\nHardware-aware inference design\n\nOriginally posted on Himalayas","description_format":"text","description_chars":2326,"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":[],"lifecycle":[{"event":"open","at":"2026-10-28T22:15:00Z"}],"visa":[],"liveness":{"score":16,"band":"cold","label":"Long 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