{"id":3685070,"url":"https://alion.io/job/physicsx-principal-machine-learning-engineer-2","title":"Principal Machine Learning Engineer (Aerospace Manufacturing)","company":{"id":251,"name":"PhysicsX","domain":"physicsx.ai","url":"https://alion.io/company/physicsx","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":82,"open_postings":36,"ghost_share":0.222,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":98,"computed_at":"2026-11-07T05:47:08Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"FastAPI","optional":false},{"name":"Kubeflow","optional":false},{"name":"Machine Learning","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"SciPy","optional":false}],"status":"live","first_seen_at":"2026-11-03T14:26:26Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-07T08:24:08Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":3},"description":"About us\nRe-architecting Engineering for the Age of Intelligence\n\nPhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area.\nWho We're Looking For\nAs a Principal Machine Learning Engineer in Delivery, you are an experienced problem solver and technical leader who stays anchored to impact. You can grasp advanced engineering concepts across multiple industries, lead technical initiatives, and work directly with customers (often side by side on-site) to embed cutting-edge AI models into tools that are useful and used.\nFor this role, that means manufacturing and maintenance, repair and overhaul (MRO). You have put ML into production on industrial process or sensor data, not benchmarks. You know the difference between a model that scores well offline and one a process owner will accept on a qualified production line. Your data comes off equipment rather than out of a solver: it is sparse, drifting, miscalibrated and partly unrecorded, and you know how to work with it.\nYou've shipped ML systems end to end and at scale: you design, build and test reliable, scalable ML data pipelines, select the right libraries, frameworks and tools, and make pragmatic product decisions that set Delivery up for success. Working at the intersection of data science and software engineering, you translate R&D and project outputs into reusable libraries, tooling and products.\nWith at least 5 years of industry experience (post Masters or PhD) in a commercial, non-research environment, you're ready not only to execute but to lead and mentor others.\nThis Role\nYou'll work closely with Data Scientists, Simulation Engineers, Manufacturing Process Experts and customers to define the process and physics problems we are solving, and you'll drive decisions on reliable deployment with measurable outcomes on the shop floor.\nYou'll:\nOwn the ML approach to manufacturing and MRO data, from modelling through deployment in customer production environments (cloud, on-prem and edge).\nBuild production ML on sparse, drifting, miscalibrated process and sensor data (process logs, sensor traces, inspection records, small DoE sets), and judge whether the data is enough to train on.\nChoose between physics simulation, reduced-order and learned models, and fuse simulation with shop-floor measurement where the two disagree.\nSet the UQ and validation standard for what a process recommendation may claim against a qualified process window, and design experiments where machine time is the cost.\nMake models transfer across machines, material lots and consumable batches.\nDefine the product requirements that manufacturing and MRO place on the PhysicsX platform.\nLead scoping and architecture for data and ML systems, and communicate results and trade-offs to senior stakeholders and process owners.\nBuild end-to-end training and inference pipelines and APIs (Python, PyTorch, Pandas, FastAPI, SciPy, Kubeflow, among others), including for 3D point-cloud and mesh data, and mentor engineers and data scientists.\nTravel to customer sites worldwide, on average 3-4 weeks per quarter, and take part in scoping new projects and bringing new customers to PhysicsX.\nWhat you've done\nWe hire on what you've done, so strong candidates can point to things like:\nBuilt and deployed ML on manufacturing process data (parameter logs, sensor traces, inspection records or small DoE sets) and can say what changed in production as a result\nFused simulation output with shop-floor measurement where the two disagreed\nSet UQ and validation standards that other teams followed\nShaped a product or tooling roadmap based on what a site or company actually needed\nHad a recommendation rejected by a process owner, and understood why\nExperience in aerospace or another regulated manufacturing environment is a plus. It is not required, because the must-have bar can be met in any sector where data comes off equipment.\nOur delivery teams drive innovation to turn AI models into practical solutions - read our blog to learn more about how you’ll contribute to this exciting journey!\nWhat we offer\nBuild what actually matters\nHelp shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind.\nLearn alongside exceptional people\nWork with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home.\nInfluence over hierarchy\nWe operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected.\nSustainable pace, long-term ambition\nBuilding meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our New York office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.\nAnd it doesn’t stop there …\n Equity options - share meaningfully in the company you’re helping to build.\n 5% contribution to 401(k) - build long-term security with a strong retirement plan.\n Free team lunch 1x/week - good food, great company, and space to connect.\n Private health insurance - comprehensive cover for you, offering total peace of mind.\n Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most.\n 20 days of Annual Leave (+ Public Holidays) - because taking time to rest matters.\n Personal development - dedicated support for learning, development, and leveling up over time.\n Gympass / Wellhub (subsidized) - for you and up to 3 family members, supporting both physical and mental wellbeing.\n Flexible Spending Account (FSA) - set aside pre-tax dollars for eligible healthcare expenses.\n Watch this space, we’re continuing to build this as we grow…\nWe value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics. \nWe collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with employment and equality legislation. 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