{"id":765939,"url":"https://alion.io/job/metamorphic-ml-research-engineer-model-training","title":"ML Research Engineer (Model Training)","company":{"id":679134,"name":"METAMORPHIC","domain":"metamorphic.com","url":"https://alion.io/company/metamorphic-2","size_band":"51-200","is_staffing_agency":false,"is_intermediary":false,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":9,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-09-24T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"hiring_geo_confidence":"structured","locations":["Palo Alto, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":175000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Docker","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"huggingface_hub","optional":true},{"name":"Knowledge Distillation","optional":true},{"name":"KV Cache","optional":true},{"name":"Model Distillation","optional":true},{"name":"Quantization","optional":true},{"name":"Ray","optional":true},{"name":"SkyPilot","optional":true},{"name":"Time Series Forecasting","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-03-26T21:10:46Z","employer_posted_date":"2026-03-26","last_verified_at":"2026-09-24T07:31:53Z","board_verified":true,"closed_at":null,"days_open":181,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":181},"description":"About Metamorphic\nMetamorphic is developing new approaches to intelligence by combining machine learning with large-scale experimental neuroscience, informed by the principles that make the brain efficient, flexible, and robust. We are building foundation models trained on rich, continuous neural data - a high-resolution model of the brain at a scale never before possible.\nOur founding team spans machine learning, neuroscience, and neurotechnology, with prior work including the MICrONS project, Neuropixels, and the Enigma project, as well as foundational scientific contributions in learning, neural computation, and generative modeling. Our work sits at the frontier of AI research, and we believe the highest-impact discoveries will come from researchers and engineers working as a single, tightly collaborative team.\nThe name Metamorphic reflects our belief that the next advances in intelligence will come from a change in form, beyond scale - from artificial to natural intelligence.\nAbout the Role\nWe are hiring Research Engineers to build end-to-end systems that take data from scientific databases all the way through to trained models, evaluation outputs, and optimized production-ready inference. This means building and maintaining the pipelines, orchestration layers, compute infrastructure, and operational tooling that make large-scale multimodal model development reliable, observable, and fast. The role spans workflow orchestration, GPU compute management, experiment execution, evaluation, model artifact management, inference optimization, and production serving. You will design the systems that coordinate complex ML workflows across heterogeneous infrastructure, support rapid iteration by researchers, and ensure that models move cleanly from experimentation into robust, low-latency, cost-efficient deployment. You'll have substantial autonomy to shape foundational technical decisions on a small, high-impact team.\nYou'll thrive in this role if you:\nAre excited about building the systems that make frontier ML research possible, reliable, and fast\n\nPrefer deeply engineering-focused work and enjoy owning production-quality systems end to end\n\nAre comfortable moving across the stack, from orchestration and infrastructure to model runtime and serving interfaces\n\nThrive in fast-paced environments where priorities can shift toward the most important operational or research need\n\nEnjoy debugging ambiguous, high-leverage problems that span multiple technical layers\n\nCare about building tooling and abstractions that make researchers dramatically more effective\n\nAre enthusiastic about working as part of a single, deeply collaborative team pursuing large-scale AI research\n\nWe offer:\nThe chance to work on one of the most scientifically consequential AI projects being pursued today\n\nA small, world-class team where your contributions directly shape the science and the company\n\nCompetitive compensation and benefits, along with visa sponsorship\n\nStrong mentorship and career development\n\nSalary Range\n$175,000 - $250,000 USD\nBased on experience. We additionally offer a competitive equity package and comprehensive benefits, as well as visa sponsorship for international candidates.\nMinimum Qualifications\nBachelor’s degree or higher in Computer Science, Machine Learning, Computational Neuroscience, or a related field\n\nStrong software engineering skills in Python and deep familiarity with PyTorch and modern machine learning workflows\n\nExperience building and operating production-grade ML systems, platforms, or pipelines that support model development at scale\n\nExperience with workflow orchestration frameworks and designing reliable multi-stage pipelines for complex ML or data systems\n\nExperience with MLOps practices including experiment tracking, artifact management, model versioning, reproducibility, and deployment workflows\n\nExperience with containerization technologies such as Docker and Kubernetes\n\nExperience with distributed compute environments for training, evaluation, and/or inference workloads in research or production settings\n\nStrong debugging skills across multiple layers of the stack\n\nExperience building or optimizing model inference and serving pipelines\n\nExperience building observability, monitoring, and logging systems for ML infrastructure\n\nNice to Have\nExperience with compute orchestration frameworks (e.g. Kubernetes, Ray, SageMaker, SkyPilot)\n\nExperience with multimodal data pipelines spanning video, time-series, and structured scientific data\n\nExperience with model registries and artifact sharing systems (e.g. HuggingFace Hub, W&B Registry)\n\nExperience with inference optimization techniques (e.g. quantization, distillation, KV-cache optimization)\n\nBackground as a systems engineer, platform engineer, or infrastructure engineer supporting ML workloads\n\nWe encourage you to apply even if you do not believe you meet every single qualification. If you don't see a role that fits, we encourage you to submit a general application and tell us how you'd like to contribute to our mission.","description_format":"text","description_chars":5066,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-11T19:59:26Z"}],"liveness":{"score":6,"band":"cold","label":"Long shot","p_open":1,"p_active":0.206,"p_room":0.28,"age_days":181,"expected_fill_days":43,"reasons":["conf:7","win:tail","crowd:"],"computed_at":"2026-09-24T05:45:00Z"},"pay":{"stated_usd_annual":250000,"is_top_pay":true},"html_url":"https://alion.io/job/metamorphic-ml-research-engineer-model-training","json_url":"https://alion.io/job/metamorphic-ml-research-engineer-model-training.json","meta":{"generated_at":"2026-09-24T09:25:10Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}