{"id":3425495,"url":"https://alion.io/job/featherless-ai-machine-learning-engineer-inference-optimization","title":"Machine Learning Engineer — 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":"unspecified","remote_scope_basis":null,"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":"CUDA","optional":false},{"name":"KV Cache","optional":false},{"name":"Machine Learning","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"ROCm","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"Triton","optional":false},{"name":"LLM","optional":true},{"name":"ONNX Runtime","optional":true},{"name":"TensorRT","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-09-24T07:14:15Z","employer_posted_date":null,"last_verified_at":"2026-09-24T07:14:15Z","board_verified":false,"closed_at":null,"days_open":37,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":37},"description":"About the RoleWe’re looking for a Machine Learning Engineer to own and push the limits of model inference performance at scale. You’ll work at the intersection of research and production—turning cutting-edge models into fast, reliable, and cost-efficient systems that serve real users.\nThis role is ideal for someone who enjoys deep technical work, profiling systems down to the kernel/GPU level, and translating research ideas into production-grade performance gains.\nWhat You’ll Do\nOptimize inference latency, throughput, and cost for large-scale ML models in production\n\nProfile and bottleneck GPU/CPU inference pipelines (memory, kernels, batching, IO)\n\nImplement and tune techniques such as:\n\nQuantization (fp16, bf16, int8, fp8)\n\nKV-cache optimization & reuse\n\nSpeculative decoding, batching, and streaming\n\nModel pruning or architectural simplifications for inference\n\nCollaborate with research engineers to productionize new model architectures\n\nBuild and maintain inference-serving systems (e.g. Triton, custom runtimes, or bespoke stacks)\n\nBenchmark performance across hardware (NVIDIA / AMD GPUs, CPUs) and cloud setups\n\nImprove system reliability, observability, and cost efficiency under real workloads\n\nWhat We’re Looking For\nStrong experience in ML inference optimization or high-performance ML systems\n\nSolid understanding of deep learning internals (attention, memory layout, compute graphs)\n\nHands-on experience with PyTorch (or similar) and model deployment\n\nFamiliarity with GPU performance tuning (CUDA, ROCm, Triton, or kernel-level optimizations)\n\nExperience scaling inference for real users (not just research benchmarks)\n\nComfortable working in fast-moving startup environments with ownership and ambiguity\n\nNice to Have\nExperience with LLM or long-context model inference\n\nKnowledge of inference frameworks (TensorRT, ONNX Runtime, vLLM, Triton)\n\nExperience optimizing across different hardware vendors\n\nOpen-source contributions in ML systems or inference tooling\n\nBackground in distributed systems or low-latency services\n\nWhy Join Us\nReal ownership 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