{"id":923644,"url":"https://alion.io/job/collinear-mts-ai-physics-simulations","title":"MTS - AI Physics & Simulations","company":{"id":673970,"name":"Collinear","domain":"collinear.ai","url":"https://alion.io/company/collinear","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-08T05:47:12Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States","Sunnyvale, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":129000,"max_usd":281000,"period":"year","method":"role_country_seniority_unknown","sample_n":3718},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Abaqus","optional":false},{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"ANSYS","optional":false},{"name":"Digital Twin","optional":false},{"name":"HPC","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenFOAM","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"CI/CD","optional":true}],"status":"live","first_seen_at":"2026-09-14T11:42:27Z","employer_posted_date":"2026-09-14","last_verified_at":"2026-11-08T18:46:48Z","board_verified":true,"closed_at":null,"days_open":55,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":55},"description":"Collinear.AI is seeking a Member of Technical Staff (Applied Scientist) with deep expertise in engineering sciences to work at the frontier of AI-accelerated simulation. In this role, you will collaborate with customers and internal research teams to build, test, and deploy AI Physics Models.\nYou will contribute across the full stack: curating high-fidelity simulation datasets, training and evaluating physics-informed models, and delivering production-grade AI solutions directly to engineering teams. Key target domains include computational fluid dynamics (CFD), structural mechanics, semiconductor design, multi-physics modeling, and digital twins.\nWorking cross-functionally across research, product, and client-facing teams, you will ensure models meet rigorous real-world engineering standards-not just theoretical benchmark metrics.\nKey Responsibilities\nExecute Simulation Campaigns: Design and orchestrate large-scale simulation campaigns using domain-specific solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus).\n\nTrain & Validate Models: Train AI models on physics datasets and conduct rigorous evaluations of coverage, accuracy, and output quality against industrial validation standards.\n\nBuild Infrastructure & Tooling: Develop robust automated frameworks for dataset creation, simulation pipeline orchestration, and continuous model evaluation.\n\nIntegrate LLMs & Workflows: Architect agentic workflows and Retrieval-Augmented Generation (RAG) systems that seamlessly connect LLMs with engineering simulation pipelines.\n\nResearch Collaboration: Partner closely with the research team to analyze training runs, diagnose failure modes, and address data sparsity or architecture bottlenecks.\n\nTechnical Project Management: Lead research initiatives and manage technical communications with external engineering teams.\n\nCore Qualifications\nEducation: Ph.D. or Master's degree in Machine Learning, Mechanical Engineering, Electrical Engineering, Computational Physics, Structural Mechanics, Semiconductor Engineering, or a related field.\n\nTechnical Mastery: Solid grounding in deep learning principles paired with a strong foundation in physics or engineering sciences.\n\nFramework Proficiency: Hands-on experience implementing and training deep learning models.\n\nSoftware Engineering: Demonstrated ability to write clean, maintainable Python in Linux and High-Performance Computing (HPC) environments.\n\nCommunication: Outstanding verbal and written communication skills, with the ability to explain complex technical concepts to both specialized engineers and non-technical stakeholders.\n\nOwnership & Mindset: Self-directed operator who thrives with autonomy, maintains a low-ego approach to collaboration, and excels in fast-paced environments at the intersection of simulation and ML.\n\nPreferred Qualifications\nHands-on industrial or academic experience with simulation solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus).\n\nDirect experience applying machine learning to physics simulations or surrogate modeling (e.g., Neural Operators, Physics-Informed Neural Networks).\n\nTrack record of automating large-scale simulation workloads on HPC clusters.\n\nMeaningful contributions to large-scale open-source projects or production codebases.\n\nPublished research in top-tier machine learning (NeurIPS, ICLR, ICML) or computational engineering conferences/journals.\n\nStrong software engineering discipline, including static typing, unit testing, and CI/CD 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