{"id":694771,"url":"https://alion.io/job/featherless-machine-learning-engineer-training-optimization","title":"Machine Learning Engineer — Training Optimization","company":{"id":673367,"name":"Featherless","domain":"featherless.ai","url":"https://alion.io/company/featherless","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"C","score":60,"open_postings":16,"ghost_share":0.75,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":7,"computed_at":"2026-10-30T05:48:23Z"}},"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":"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":"FSDP","optional":false},{"name":"PyTorch","optional":false},{"name":"DeepSpeed","optional":true},{"name":"Megatron-LM","optional":true}],"status":"live","first_seen_at":"2026-01-22T05:26:16Z","employer_posted_date":"2026-01-22","last_verified_at":"2026-10-30T13:41:39Z","board_verified":true,"closed_at":null,"days_open":281,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":279},"description":"About the Role\nWe’re looking for an ML Engineer focused on training optimization to help us scale and improve large-scale model training. You’ll work at the intersection of research and production, optimizing training pipelines for speed, stability, and cost-while collaborating closely with researchers pushing model architecture and capability forward.\nThis is a high-impact role with real ownership: your work directly affects how fast we can iterate, how large we can scale, and how efficiently we deploy new models.\nWhat You’ll Do\nOptimize large-scale model training pipelines (throughput, convergence, stability, and cost)\n\nImprove distributed training strategies (data, model, and pipeline parallelism)\n\nTune optimizers, schedulers, batch sizing, and precision (bf16 / fp16 / fp8)\n\nReduce training time and compute cost via profiling, bottleneck analysis, and systems-level improvements\n\nCollaborate with researchers on architecture-aware training strategies\n\nBuild and maintain robust training infrastructure (checkpointing, fault tolerance, reproducibility)\n\nEvaluate and integrate new training techniques (e.g. gradient checkpointing, ZeRO, FSDP, custom kernels)\n\nOwn training performance metrics and continuously push them forward\n\nWhat We’re Looking For\nStrong experience training large neural networks (LLMs or similarly large models)\n\nHands-on experience with training optimization (not just model usage)\n\nSolid understanding of:\nBackpropagation, optimization algorithms, and training dynamics\n\nDistributed systems for ML training\n\nExperience with PyTorch (required)\n\nComfort working close to hardware (GPUs, memory, networking constraints)\n\nAbility to move fluidly between research ideas and production-ready code\n\nNice to Have\nExperience with large-scale distributed training (multi-node, multi-GPU)\n\nFamiliarity with DeepSpeed, FSDP, Megatron, or custom training stacks\n\nExperience optimizing training on AMD or NVIDIA GPUs\n\nContributions to open-source ML infrastructure or research codebases\n\nExposure to non-Transformer architectures (RNNs, hybrid models, etc.)\n\nWhy Join Us\nReal ownership at Series-A stage - 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