{"id":1204636,"url":"https://alion.io/job/laminar-ml-infrastructure-engineer","title":"ML Infrastructure Engineer","company":{"id":689765,"name":"Laminar","domain":"runlaminar.com","url":"https://alion.io/company/runlaminar","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-09-27T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Somerville, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":89000,"max":141000,"currency":"USD","period":"year","gross":null,"usd_annual":141000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Boto3","optional":false},{"name":"Databricks","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Weights & Biases","optional":false}],"status":"live","first_seen_at":"2026-09-24T14:51:06Z","employer_posted_date":"2026-09-24","last_verified_at":"2026-09-28T01:07:23Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"As our company grows and scales, we are excited for a ML Infrastructure Engineer to join the team! We are looking for a thoughtful and hard-working infrastructure engineer who wants to play an integral role in bringing AI to fluid & process manufacturing. As a ML Infrastructure Engineer, you will own the development of infrastructure and tooling that helps ML researchers train, evaluate, and deploy models at scale. Your work will directly power the vertical and horizontal scalability of Laminar’s ML models across domains including (bot not limited to): CIP (clean-in-place), product changeovers, material identification, product filtration, and emerging use-cases.\nYou will interface with ML researchers and data engineers to build infrastructure that allows researchers to frictionlessly train models on large-scale data, evaluate them on unseen data, and deploy champion models to run on the factory floor across edge devices. Your tooling will be fundamental to making our research-to-production ML pipeline faster and more hands-free, ensuring a seamless experience for researchers. Your work will be instrumental to hyper-scaling Laminar’s solutions and deepening our competitive moat by empowering researchers to deliver state-of-the-art technological advancements.\nWhat You Will Do\nDevelop computer orchestration tooling for researchers to seamlessly launch modeling jobs on large-scale data - training, fine-tuning, inference.\n\nDesign model testing environments that automatically evaluate model performance without a human in the loop through semi-supervised metrics and process-aware priors.\n\nBuild model registries and automated deployment pipelines that support large-scale model tracking, versioning, and deployment on edge devices.\n\nDevelop monitoring tools for deployed models: detect model drift or anomalies, then trigger continuous training (CT) pipelines as needed.\n\nWork with ML researchers, ML developers to design systems that meet their needs; work with software engineers to design systems that interact gracefully with existing infrastructure.\n\nBuild for our unique use-cases and problems - not for the average problem.\n\nAbout You\nHighly experienced using cloud platforms (AWS, Databricks) to train and evaluate ML models on large-scale data.\n\nExperienced using off-the-shelf tools (MLflow, wandb) for experiment tracking and model lifecycle management (versioning, artifact registry, deployment, monitoring).\n\nHighly experienced with Python and relevant SDKs (boto3, databricks-sdk, mlflow); familiar with modern ML frameworks (jax, pytorch).\n\nFamiliar accessing data through SQL, Databricks/Apache Spark, and raw parquet formats.\n\nAn engineer who thrives on building easy-to-use tools that researchers love to use.\n\nHighly detail-oriented: you understand the nuances in our workflows and respect the challenges that come with large-scale ML training and deployment to edge devices.\n\nOpen-minded and independent thinker - well-versed in building tailor-made solutions that address real pain points.\n\nAn executor who can both independently complete technical project objectives and provide domain expertise to guide engineering design decisions.\n\nPreferred(if any)\nChemical engineering, process engineering, or manufacturing domain knowledge (highly valued).\n\nPast experience working with spectral data, time-series data, or sensor data.\n\nExperience building or evaluating custom ML models.\n\nExperience building real products and practicing user-centric design.\n\nBenefits\nDirect impact on product and culture.\nComprehensive benefits package including Medical, Dental, Vision, Life Insurance, Disability, Transportation benefit, Health and Wellness benefit, and more.\n401k plan with employer matching\nEquity\nCompetitive salary and bonus opportunities.\nDynamic and inclusive work environment.\nOpportunities for growth and professional development.\nAccess to Greentown Labs' extensive network of cleantech startups.\nLearn How We Think\nLearn about our startup journey: Our Journey\nHow we're combating climate change: AI-Powered Climate Tech","description_format":"text","description_chars":4067,"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":["401k plan","Equity","Life insurance","Professional development"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Sensors & Transducers","Industrial AI"],"lifecycle":[{"event":"open","at":"2026-09-24T23:01:11Z"}],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":2,"expected_fill_days":108,"reasons":["conf:1","velocity","win:early"],"computed_at":"2026-09-27T05:45:00Z"},"pay":{"stated_usd_annual":141000,"is_top_pay":false},"html_url":"https://alion.io/job/laminar-ml-infrastructure-engineer","json_url":"https://alion.io/job/laminar-ml-infrastructure-engineer.json","meta":{"generated_at":"2026-09-28T02:38:30Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":1371,"day_limit":5000,"remaining_today":3629,"minute_limit":60,"resets_at":"2026-09-29T00:00:00Z"}}}