Research Scientist - Long-Horizon Dynamic AI Modeling

Becoming; San Francisco (CA); On-site; USD 150,000 - 230,000; Full time

Benefits offered by this job

Full benefits

Competitive salary

Job summary

Becoming is building Developmental Intelligence: AI for predicting how organisms change over time. We are hiring a Research Scientist to rigorously evaluate a new modeling primitive for complex, time-evolving systems, beyond incremental improvements.

Your work will influence how the platform develops, its scope, and applicability across domains with dynamic complexity. You will design experiments, compare against existing approaches, identify failure modes, and articulate why the model succeeds

Qualifications

• PhD or equivalent in applied mathematics, physics, computer science, machine learning, or a related field.
• At least 1 year of industry or applied research experience on real modeling systems.
• Strong grounding in dynamical systems, time-series modeling, control, or sequence modeling.
• Ability to design validation strategies when standard benchmarks are insufficient.

Responsibilities

• Validate a new AI modeling primitive for long-horizon dynamical systems.
• Design experiments and benchmarks testing stability, generalization, and predictive fidelity over time.
• Conduct comparative evaluation against existing modeling approaches.
• Identify failure modes, assumptions, and edge cases; articulate why models succeed or fail in different regimes.
• Translate findings into guidance for future architecture and system design.
• Explore applicability across multiple domains with complex dynamics, not just biology.

Skills

Dynamical systems

Time-series modeling

Validation design

Experimentation

Analytical reasoning

Education

PhD or equivalent in applied math / physics / CS / ML

Job description

Becoming is building Developmental Intelligence: AI for predicting how organisms change over time. We are hiring a Research Scientist to rigorously evaluate a new modeling primitive for complex, time-evolving systems, beyond incremental improvements.

Your work will influence how the platform develops, its scope, and applicability across domains with dynamic complexity. You will design experiments, compare against existing approaches, identify failure modes, and articulate why the model succeeds