{"id":701002,"url":"https://alion.io/job/periodic-computational-scientist-differentiable-physics","title":"Computational Scientist, Differentiable Physics","company":{"id":685855,"name":"Periodic Labs","domain":"periodic.com","url":"https://alion.io/company/periodic","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":85,"open_postings":31,"ghost_share":0.258,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":51,"computed_at":"2026-11-02T06:13:15Z"}},"role":"Science & Research","role_family":"Science & Research","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":["Menlo Park, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":250000,"max":350000,"currency":"USD","period":"year","gross":null,"usd_annual":350000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"JAX","optional":false},{"name":"Julia","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TPU","optional":false}],"status":"live","first_seen_at":"2026-09-04T17:25:00Z","employer_posted_date":"2026-09-04","last_verified_at":"2026-11-02T17:44:18Z","board_verified":true,"closed_at":null,"days_open":59,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":58},"description":"About the Role\nPeriodic Labs is building AI systems that can simulate physical science, verify predictions, and train on the full scientific method. We are looking for a Computational Scientist to build differentiable, accelerator-ready simulations for industrially relevant continuum-physics problems.\nYou should be equally comfortable with governing equations, solver code, and deep learning. We are open to expertise in any area of continuum-physics, with at least some experience in fluid dynamics. You will work on building simulation capabilities in challenging, data-limited domains requiring a mix of physics-based and empirical approaches.\nWhat You’ll Do\nBuild and extend differentiable solvers for continuum simulation (including but not limited to fluid dynamics), especially multi-scale and multi-physics problems.\n\nImplement numerical methods from equations and papers, and diagnose convergence, stability, and modeling failures.\n\nCombine simulation with deep learning for surrogate modeling, learned physics, inverse problems, parameter estimation, and optimization.\n\nUse automatic differentiation and modern accelerators with JAX or PyTorch to make simulations scalable and trainable.\n\nValidate models against experiments, trusted benchmarks, or high-fidelity simulations.\n\nCreate datasets and evaluations to guide the development of LLMs to accelerate and automate these tasks.\n\nYou Will Thrive Here If You Have\nA PhD or equivalent research experience in applied mathematics, computational science, physics, engineering, computer science, or a related field.\n\nCode-level experience building or substantially modifying PDE solvers, numerical methods, or differentiable simulations.\n\nDeep expertise in at least one continuum domain, with breadth across domains or a demonstrated ability to learn new physics quickly.\n\nMeaningful experience building, training, and evaluating deep-learning models for physical systems.\n\nStrong Python and software-engineering skills, especially JAX, PyTorch, Julia, or C++.\n\nExperience applying simulation to realistic scientific or engineering problems, not only clean academic benchmarks.\n\nA startup mentality: ownership, good judgment under uncertainty, and enthusiasm for building from scratch.\n\nStrong Candidates May Also Have\nExperience with fluid dynamics plus another continuum domain, or with multiphysics and multiscale modeling.\n\nExpertise in adjoint methods, implicit differentiation, differentiable programming, or scientific optimization.\n\nExperience accelerating scientific software on GPUs or TPUs.\n\nContributions to scientific open-source software used by others.\n\nExperience connecting simulation to experiments, engineering decisions, semiconductors, or autonomous workflows.\n\nMechanics\nMinimum education: Bachelor's degree or similar experience\n\nLocation: Menlo Park, CA\n\nCompensation: $250,000-350,000 + equity\n\nVisa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process.","description_format":"text","description_chars":2975,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["AI Research Labs","AI for 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