{"id":701019,"url":"https://alion.io/job/periodic-research-scientist-data","title":"Research Scientist, Data","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":86,"open_postings":31,"ghost_share":0.226,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":51,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Menlo Park, United States","Montreal, Canada"],"countries":["US","CA"],"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":"LLM","optional":false},{"name":"Post-training","optional":false},{"name":"Reinforcement Learning","optional":false}],"status":"live","first_seen_at":"2026-07-25T00:34:40Z","employer_posted_date":"2026-07-25","last_verified_at":"2026-10-29T04:10:03Z","board_verified":true,"closed_at":null,"days_open":96,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":96},"description":"About Periodic Labs\nThe most important scientific discoveries of our time won’t happen in a traditional lab. We’re an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what’s scientifically possible.\nAbout the Role\nYou will work on the most important aspect of Scientific AI creation: evaluations and data. This means constructing cutting-edge evaluations based on advanced scientific use cases, sourcing and procuring external datasets, integrating internally generated experimental data into the training stack, constructing training environments for RL. You’ll ensure that the team always has the right assets, in the right shape, to evaluate and improve AI models.\nYou will work with computational and experimental scientists to translate complex scientific workflows into rigorous evaluations and agentic benchmarks, and partner with pretraining, midtraining, and reinforcement learning researchers to identify the data models needed, then build the datasets, environments, and pipelines to deliver it. Your goal will be to create a tight feedback loop between scientific use cases, model evaluation, and training data.\nWhat You’ll Do\nOwn the evaluation and data strategy across the training stack, identifying capability gaps and shaping the roadmap with leads of physical science and AI research\n\nWork with domain experts to translate advanced scientific workflows into rigorous evals, benchmarks, and RL environments\n\nSource, evaluate, and procure external datasets across chemistry, physics, materials science, mathematics, simulations, and laboratory instrumentation\n\nBuild robust pipelines to ingest, clean, and transform for training large-scale datasets from heterogeneous sources\n\nBuild tooling and analysis workflows that help researchers inspect data, understand model failures, and determine which evaluations or datasets to develop next\n\nYou Will Thrive in This Role If You Have\nDesigned evaluations, benchmarks, or RL environments for language models, agents, or scientific AI systems\n\nBuilt large-scale data pipelines for LLM pretraining, midtraining, post-training, or evaluation\n\nStrong judgment about dataset and evaluation quality, including scientific relevance, coverage, provenance, licensing, and contamination risks\n\nStrong software and data engineering skills, including familiarity with data processing at scale, dataset versioning, lineage tracking\n\nA research-oriented mindset: you form hypotheses about data, run controlled experiments, measure model outcomes, and iterate with rigor\n\nResearch experience in areas such as materials science, solid state chemistry, chemistry, computational physics, semiconductors\n\nMechanics\nMinimum education: Bachelor’s degree or similar experience\nLocation: Menlo Park, CA or Montreal, Canada\nCompensation: $250,000-350,000 + equity\nVisa sponsorship: Yes, we sponsor 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