{"id":838850,"url":"https://alion.io/job/becoming-research-scientist-dynamical-systems-ai","title":"Research Scientist, Dynamical Systems & AI","company":{"id":670153,"name":"Becoming","domain":"becoming.ai","url":"https://alion.io/company/becoming","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":83,"open_postings":3,"ghost_share":0,"stale_share":0.667,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-22T06:10:10Z"}},"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":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":170000,"max_usd":375000,"period":"year","method":"role_country_seniority_unknown","sample_n":4194},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Design of Experiments","optional":false},{"name":"Machine Learning","optional":false},{"name":"Time Series","optional":false},{"name":"World Models","optional":false}],"status":"live","first_seen_at":"2026-08-21T00:00:00Z","employer_posted_date":"2026-08-21","last_verified_at":"2026-10-23T04:44:24Z","board_verified":true,"closed_at":null,"days_open":63,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":62},"description":"About Becoming\nBecoming is building Developmental Intelligence: AI for predicting how organisms change over time.\nMost existing models work in short-horizon or static regimes. They fail when systems become long-horizon, nonlinear, and context-dependent - exactly where development, biology, and many real-world systems live.\nWe are building a new modeling primitive for reasoning about complex dynamics over time, starting with developmental biology and extending to other domains where prediction fundamentally breaks.\nThe Role\nWe are hiring a Research Scientist to help verify, validate, and shape a new modeling primitive designed for complex, time-evolving systems.\nThis role is not about incremental model improvements or benchmark chasing. Your responsibility is to rigorously evaluate whether this primitive works, where it works, where it fails, and why. You will help define its scope, limits, and evolution through careful analysis, experimentation, and comparison to existing approaches.\nYour work will directly influence how the platform develops and how broadly it can be applied.\nWhat You’ll Own\nValidation of a new AI modeling primitive for long-horizon dynamical systems\nDesign of experiments and benchmarks that test stability, generalization, and predictive fidelity over time\nComparative evaluation against existing modeling approaches\nIdentification of failure modes, assumptions, and edge cases\nClear articulation of why the model succeeds or fails in different regimes\nTranslation of findings into guidance for future architecture and system design\nExploration of applicability across multiple domains with complex dynamics, not just biology\nWho You Are\nYou are someone who:\nOperates with high agency - you identify problems, define solutions, and execute\nBrings high energy to complex, ambiguous engineering challenges\nActs with high integrity - you are honest about tradeoffs, risks, and failure modes\nCommunicates directly and clearly, especially when something won’t work\nIs self-aware about your strengths and gaps, and proactively fills them\nThinks naturally in terms of dynamics, stability, and generalization\nEnjoys stress-testing models to understand their limits\nIs comfortable working on foundational problems with ambiguous answers\nRequirements\nRequired\nPhD or equivalent experience in applied mathematics, physics, computer science, machine learning, or a related field\nAt least 1 year of industry or applied research experience working on real modeling systems\nStrong grounding in dynamical systems, time-series modeling, control, or sequence modeling\nExperience evaluating models where ground truth is partial, delayed, or noisy\nAbility to design validation strategies when standard benchmarks are insufficient\nComfort working with first-of-its-kind architectures and open-ended questions\nStrong Signals\nExperience with state-space models, world models, neural ODEs, diffusion over time, or hybrid approaches\nPrior work on systems where prediction degrades over long horizons\nExposure to biological, physical, or other real-world dynamical systems\nTrack record of identifying why models fail - 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