{"id":211942,"url":"https://alion.io/job/physicsx-senior-applied-engineer","title":"Senior Applied 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":88,"open_postings":41,"ghost_share":0.122,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":119,"computed_at":"2026-10-02T05:45:00Z"}},"role":"Industrial Engineering","role_family":"Industrial Engineering","seniority":"senior","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":{"min_usd":45000,"max_usd":96000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":239},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"C++","optional":false},{"name":"CI/CD","optional":false},{"name":"Computer Vision","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Dask","optional":false},{"name":"Docker","optional":false},{"name":"Federated Learning","optional":false},{"name":"GCP","optional":false},{"name":"JAX","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"OpenMP","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"SciPy","optional":false},{"name":"SLURM","optional":false},{"name":"Spark","optional":false},{"name":"Triton","optional":false}],"status":"live","first_seen_at":"2026-07-08T15:55:39Z","employer_posted_date":"2026-08-26","last_verified_at":"2026-10-02T18:04:21Z","board_verified":true,"closed_at":null,"days_open":86,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":85},"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 research scientists, simulation engineers, customers and partners to deliver AI models that address real-world physics and engineering problems.\nDesign and build physical foundation models with a focus on efficiently scaling model training to large data on multi-GPU cloud compute.\nTransform prototypes from your research scientist colleagues into robust and optimised implementations, challenging architecture decisions that hurt scalability.\nIdentify and argue for the best libraries, frameworks and tools to set us up for success.\nOwn Research work-streams at different levels, depending on seniority.\nDiscuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems.\nWork at the intersection of data science and software engineering to translate the results of our Research into re-usable libraries, tooling and products.\nFoster curiosity and initiative among your colleagues and mentees.\nWhat you bring to the table\nEnthusiasm about developing machine learning solutions, especially deep learning and/or probabilistic methods, and associated supporting software solutions for science and engineering.\nAbility to work autonomously and scope and effectively deliver projects across a variety of domains.\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.\nMSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, software engineering, or a related field, with a record of experience in any of the following:Scientific computing;\nHigh-performance computing (CPU / GPU clusters);\nParallelised / distributed training for large / foundation models.\n\nIdeally, >2 years of experience in a data-driven, professional setting, with exposure to:scaling and optimising ML models, training and serving foundation models at scale (federated learning a bonus);\ndistributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton);\ncloud computing (on hyper-scaler platforms, e.g., AWS, Azure, GCP);\nbuilding machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications;\nC/C++ for computer vision, geometry processing, or scientific computing;\nsoftware engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps);\ncontainer-ization and orchestration (Docker, Kubernetes, Slurm);\nwriting pipelines and experiment environments, including running experiments in pipelines in a systematic way.\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. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.","description_format":"text","description_chars":5693,"description_truncated":false,"requirements":{"experience_years_min":2,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Singapore","iso":"SG","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Simulation & Digital Twin Software","Industrial AI","AI for Science"],"lifecycle":[{"event":"open","at":"2026-09-05T08:32:35Z"}],"liveness":{"score":54,"band":"ok","label":"Likely open","p_open":1,"p_active":0.892,"p_room":0.6,"age_days":85,"expected_fill_days":119,"reasons":["conf:5","velocity","win:late","crowd:"],"computed_at":"2026-10-02T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/physicsx-senior-applied-engineer","json_url":"https://alion.io/job/physicsx-senior-applied-engineer.json","meta":{"generated_at":"2026-10-03T00:02:27Z","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":29,"day_limit":5000,"remaining_today":4971,"minute_limit":60,"resets_at":"2026-10-04T00:00:00Z"}}}