{"id":3721708,"url":"https://alion.io/job/nvidia-senior-applied-research-scientist-ai-native-numerical-methods","title":"Senior Applied Research Scientist – AI Native Numerical Methods","company":{"id":6,"name":"NVIDIA","domain":"nvidia.com","url":"https://alion.io/company/nvidia","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":84,"open_postings":821,"ghost_share":0.023,"stale_share":0.56,"repost_share":0.054,"time_to_fill_p50_days":40,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":192000,"max":304750,"currency":"USD","period":"year","gross":null,"usd_annual":304750},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CUDA","optional":false},{"name":"Digital Twin","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false}],"status":"live","first_seen_at":"2026-11-03T00:00:00Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-06T15:56:12Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"At NVIDIA, we are using accelerated computing and machine learning to create a new generation of AI-native scientific, engineering, and industrial simulation. We are seeking an applied researcher who can connect machine learning with numerical algorithms to make solvers faster, more reliable, and more efficient. This work focuses on ML methods inside and around numerical solvers, including AI-guided multigrid, solver-in-the-loop learning, differentiable simulation integrated with AI algorithms, and hybrid numerical / ML methods. We work across NVIDIA solver, simulation, and computational engineering platforms, with collaborators NVIDIA Research, universities, and industrial simulation teams.\nThe primary focus of this role is to invent AI-native numerical algorithms that combine machine learning with classical scientific and engineering solvers on modern GPU architectures.\nWhat you'll be doing:\nResearch AI-assisted numerical methods that improve convergence, stability, accuracy, robustness, and wall-clock performance for large-scale scientific, engineering, and industrial simulations.\n\nInvent learned coarse spaces, learned preconditioners, AI-guided multigrid methods, sequence-aware solver strategies, solver-control policies, differentiable solver components, and hybrid numerical / ML algorithms.\n\nBuild solver-in-the-loop pipelines using residual histories, discretized operators, meshes, geometry, simulation outputs, performance counters, and physics constraints.\n\nDefine evaluation methods that measure numerical impact, including convergence rate, failure rate, conservation, memory footprint, correctness, and end-to-end speedup.\n\nCollaborate with teams across numerical methods, CUDA-X, Warp, PhysicsNeMo, NVIDIA Research, CAE, EDA, semiconductor, electronics, thermal-fluid, electromagnetics, and digital twin workflows.\n\nHelp define NVIDIA's applied research agenda for AI-native numerical methods and solver intelligence.\n\nWhat we need to see:\nPhD or equivalent experience in computer science, machine learning, scientific computing, applied mathematics, computational engineering, physics, or a related field.\n\n5 years of relevant work/research experience\n\nBackground in machine learning and scientific computing, with evidence of connecting ML methods to numerical algorithms.\n\nExperience with PyTorch, JAX, or comparable deep learning frameworks, plus Python and GPU computing.\n\nWorking knowledge of PDEs, sparse linear algebra, iterative solvers, preconditioning, finite element / finite volume methods, optimization, or differentiable programming.\n\nResearch record in scientific machine learning, numerical linear algebra, or differentiable simulation integrated with numerical solvers.\n\nCommunication skills that support collaboration across AI research, numerical methods, product, and production software teams.\n\nWays to stand out from the crowd:\nEvidence that ML methods improved real numerical solvers through faster convergence, fewer failures, improved robustness, or lower cost on industrial-scale simulations.\n\nExperience with AI-guided multigrid, reduced-order components inside solver algorithms, neural operators connected to solver workflows, or automated solver control.\n\nExperience with differentiable simulation, PDE-constrained learning, inverse design, uncertainty quantification, Bayesian methods, reinforcement learning for solver control, or automated algorithm selection.\n\nPublications, software contributions, or collaborations in scientific computing, matrix computations, industrial simulation, CAE, EDA, semiconductor simulation, electronic build, thermal-fluid simulation, electromagnetics, or digital twins.\n\nYour base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:192,000 USD - 304,750 USD for Level 4; and 224,000 USD - 356,500 USD for Level 5.\nYou will also be eligible for equity and benefits.\nApplications for this job will be accepted at least until November 7, 2026.This posting is for an existing vacancy.\nNVIDIA uses AI tools in its recruiting processes.\nNVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. 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