{"id":35117,"url":"https://alion.io/job/physicsx-principal-machine-learning-infrastructure-engineer","title":"Senior Research 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":"B","score":83,"open_postings":40,"ghost_share":0.2,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":98,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":128000,"max_usd":256000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":13},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"CoreWeave","optional":false},{"name":"CUDA","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"Triton","optional":false},{"name":"Azure","optional":true},{"name":"C++","optional":true},{"name":"Computer Vision","optional":true},{"name":"GCP","optional":true},{"name":"Octopus Deploy","optional":true}],"status":"live","first_seen_at":"2026-04-13T10:01:07Z","employer_posted_date":"2026-11-08","last_verified_at":"2026-11-13T19:53:59Z","board_verified":true,"closed_at":null,"days_open":214,"trust":{"level":"ok","repost_count":1,"flags":["company_stale"],"days_open":213},"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.\nWhat you'll do\nWork closely with our simulation engineers and research scientists to develop an understanding of the physics and engineering challenges we are solving\nWork at the intersection of data science and software engineering to transform prototype implementations to robust production-grade model implementations, libraries and tooling\nWork with multi-GPU/multi-node distributed systems for training and inference at the scale of PBs of data and hundreds of GPUs\nOptimize the models and pipelines on all levels, from job scheduling down to GPU kernels\nDesign and build scalable and robust infrastructure for end-to-end machine learning applications on cloud (CoreWeave, AWS)\nContinuously apply and improve engineering best practices and standards, coach your colleagues in their adoption.\nWhat you'll bring\nEnthusiasm about using machine learning for science and engineering, and especially in scaling such solutions to real-world settings\nAbility to work autonomously in a fast-paced environment, learn and adapt quickly, scope, plan and deliver effectively\nStrong collaboration and communication skills, especially with research scientists\nExperience with software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps)\nExperience building ML models and pipelines in Python, using common libraries and frameworks (e.g., PyTorch, TensorFlow, JAX)\nExperience with the GPU programming ecosystem, ability to optimise models on all levels of the stack, going down to the level of CUDA/Triton/Warp kernels when necessary\nExperience training and serving ML models at scale\nExperience with large-scale data processing\nNice to have\nDomain knowledge in physics and/or numerical methods, especially CFD\nExperience working in the domain of ML for science or engineering\nExperience building infrastructure on cloud platforms (e.g., AWS, Azure, GCP, CoreWeave)\nExperience with C/C++, especially for computer vision, geometry processing and scientific computing\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 Shoreditch 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 10% employer pension contribution - because investing in future matters.\n Free office lunches - to keep you energised and focused.\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 YellowNest nursery scheme - to help working parents manage childcare costs.\n 25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters.\n Private medical insurance - 100% employee cover, giving you complete peace of mind.\n Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing.\n Eye tests - because good work depends on good health.\n Personal development - dedicated support for learning, development, and leveling up over time.\n Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it.\n Bike2Work scheme and Season ticket loan - to make getting to work easier and greener.\n Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric.\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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