{"id":72523,"url":"https://alion.io/job/physicsx-machine-learning-engineer-2","title":"Machine Learning Engineer","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":"A","score":86,"open_postings":38,"ghost_share":0.158,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":96,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","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":{"min":150000,"max":190000,"currency":"USD","period":"year","gross":null,"usd_annual":190000},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":true,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Dask","optional":false},{"name":"Docker","optional":false},{"name":"Edge AI","optional":false},{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"Spark","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-04-23T20:11:11Z","employer_posted_date":"2026-09-09","last_verified_at":"2026-10-16T19:01:06Z","board_verified":true,"closed_at":null,"days_open":175,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":175},"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.\nNote: We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals.\nWho We're Looking For\nAs a Machine Learning Engineer in Delivery, you are a problem solver who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple industries, and excel at working directly with customers (and often side-by-side with them on-site) to embed cutting-edge AI models into tools that are useful and used.\nYou’ve shipped ML systems end-to-end and at scale: you design, build and test reliable, scalable ML data pipelines; you know how to explore and manipulate 3D point-cloud and mesh data to enable geometry-aware modelling; you select the right libraries, frameworks and tools. 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 2 years industry experience (post Masters or PhD) in a commercial, non-research environment. You're truly excited about taking ownership of complex work streams and guiding teams to success, while continuously improving the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers.\nNote: This position may require access to information protected under U.S. export control laws and regulations, including the Export Administration Regulations (EAR) and the International Traffic in Arms Regulations (ITAR). Please note that any offer for employment may be conditioned on authorization to receive software or technology controlled under these U.S. export control laws and regulations without sponsorship for an export license.\nThis Role \nAs a Machine Learning Engineer, you'll work closely with our Data Scientists, Simulation Engineers, and customers to understand and define the engineering and physics challenges we are solving. You will iterate with customers and use your influence to drive decisions around reliable deployment with measurable outcomes.\nWhat you will do\nWork closely with our simulation engineers, data scientists and customers to develop an understanding of the physics and engineering challenges we are solving\nDesign, build and test data pipelines for machine learning that are reliable, scalable and easily deployable\nExplore and manipulate 3D point cloud & mesh data\nOwn the delivery of technical workstreams\nCreate analytics environments and resources in the cloud or on premise, spanning data engineering and science\nIdentify the best libraries, frameworks and tools for a given task, make product design decisions to set us up for success\nWork at the intersection of data science and software engineering to translate the results of our R&D and projects into re-usable libraries, tooling and products\nContinuously apply and improve engineering best practices and standards and coach your colleagues in their adoption\nYou'll also have the opportunity to travel to customer sites in North America, Europe, Asia, Oceania, for an average of 3-4 weeks per quarter, where you'll collaborate closely with customers to build solutions on-site.\nWhat you bring to the table\nExperience applying Machine learning methods (including 3D graph/point cloud deep learning methods) to real-world engineering applications, with a focus on driving measurable impact in industry settings.\nExperience in ML/Computational statistics/Modelling use-cases in industrial settings (for example supply chain optimisation or manufacturing processes) is encouraged.\nA track record of scoping and delivering projects in a customer facing role\n2+ years’ experience in a data-driven role, with exposure to software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps)\nBuilding machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, MLFlow)\nDistributed computing frameworks (e.g., Spark, Dask)\nCloud platforms (e.g., AWS, Azure, GCP) and HP computing\nContainerization and orchestration (Docker, Kubernetes) \nStrong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly\nExcellent collaboration and communication skills - with teams and customers alike\nA background in Physics, Engineering, or equivalent\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…\nSalary range:\n$150,000 - $190,000 depending on experience \nSeniority will be assessed throughout our interview process \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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