{"id":4270527,"url":"https://alion.io/job/raydar-senior-applied-machine-learning-scientist","title":"Senior Applied Machine Learning Scientist","company":{"id":4478753,"name":"Raydar","domain":"raydar.com","url":"https://alion.io/company/raydar-com","size_band":"51-200","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":75,"open_postings":7,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":23,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","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 Mateo, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":165000,"max":295000,"currency":"USD","period":"year","gross":null,"usd_annual":295000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false}],"status":"live","first_seen_at":"2026-11-13T17:39:23Z","employer_posted_date":"2026-11-13","last_verified_at":"2026-11-13T17:39:23Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About the company\nOur client is a software company.\nThe role\nRaydar is recruiting for this role on behalf of our client. Design, train and validate machine learning models on engineering and scientific data, then work with engineers to put those models into production. The role combines hands-on model development with close work alongside client-facing and platform teams, and includes mentoring colleagues.\nWhat you'll do\n- Own model development from architecture choice through training, tuning, evaluation and validation against reference simulation results.\n- Check that training datasets are suitable and sufficient for the intended use case.\n- Work with data and platform engineers to build training pipelines, model registries and inference services.\n- Apply recent research methods from the machine learning field to practical client problems.\n- Work alongside client data scientists and engineers, sharing methods and building their modelling skills.\n- Feed lessons from delivery back to research and product teams to shape future methods and tooling.\n- Mentor junior team members and help set applied modelling practices.\nRequirements\nWhat we're looking for\n- Experience training and deploying machine learning models on scientific or engineering simulation data using Python and PyTorch.\n- Can translate complex modelling work for audiences without specialist knowledge, opening with the most important findings.\n- Experience working directly with clients or in internal consulting roles, deploying machine learning models into real engineering workflows.\n- Experience training surrogate models on simulation data where geometry and mesh vary from sample to sample.\n- Hands-on experience with mesh, graph or point-cloud model architectures for physics surrogates.\nBonus points\n- Previous work with simulation software, scientific computing or AI-focused organisations, including research laboratories, is a plus.\n- A graduate qualification in a numerate discipline, such as statistics or the hard sciences, is welcome, with a master's or doctorate valued.\nBenefits\nCompensation and benefits\n- Base salary: $165K - 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