{"id":35129,"url":"https://alion.io/job/physicsx-applied-scientist-all-levels","title":"Applied Scientist - All Levels","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":88,"open_postings":40,"ghost_share":0.125,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":96,"computed_at":"2026-10-08T05:49:30Z"}},"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":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"Diffusion Models","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"SciPy","optional":false}],"status":"live","first_seen_at":"2026-07-08T09:48:37Z","employer_posted_date":"2026-08-26","last_verified_at":"2026-10-08T21:17:57Z","board_verified":true,"closed_at":null,"days_open":92,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":91},"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.\nPhysicsX is starting a research team in Singapore to build physical foundation models alongside our customers and partners, targeting engineering domains where this capability will be most transformative.\nWhat you will do \nWork closely with our machine learning engineers, simulation engineers, customers and partners to translate physics and engineering challenges into mathematical problem formulations.\nBuild models to predict the behaviour of physical systems using state-of-the-art machine learning techniques that scale to large datasets, iterating through robust experimentation.\nChart a path through competing trade-offs with insufficient information, e.g. is it better to train a bigger model or to generate more data?\nOwn Research work-streams at different levels, depending on seniority.\nDiscuss the results and implications of your work with colleagues and customers, connecting with real-world problems.\nCommunicate your work to others internally and externally as called for in paper publication venues, industry workshops, customer conversations, etc.\nFoster curiosity and initiative among your colleagues and mentees.\nWhat you bring to the table\nEnthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering.\nAbility to scope and effectively deliver projects.\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.\nPhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following:operator learning (neural operators), or other probabilistic methods for PDEs;\ngeometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data;\ngenerative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.).\n\nIdeally, >2 years of experience in a data-driven role, with exposure to:building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications;\ndeveloping models for bespoke problem settings that involve high-dimensional data (spatiotemporal, geometric, physical);\niterating on network architectures and model structure, tuning and optimising for inductive biases, improved generalisability, and improved performance;\ncombining theoretical reasoning with empirical intuition to guide investigation;\nformulating and running experiment pipelines to benchmark models and produce comparable results;\nwriting skills for communicating complex technical concepts to peers and non-peers, tailoring the message for the required audience.\n\nPublication record in reputable venues that demonstrates mastery in your field, and in particular the domains of interest listed above. Desirable venues include (but not limited to): NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, Siggraph, CVPR, TPAMI/JMLR, Nature and Science.\n\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.\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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