Research Scientist, Long-Horizon Dynamical AI

Becoming; San Francisco (CA); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Competitive salary

Equity

Full benefits

High ownership

Career growth

Job summary

Becoming in San Francisco seeks a Research Scientist to rigorously evaluate a new modeling primitive designed for complex, time-evolving systems. The role focuses on validating scope, limits, and evolution through careful analysis, experimentation, and comparison to existing approaches.

Your work will directly influence platform development and its application across domains with dynamic, long-horizon behavior, demanding clear articulation of failure modes and robust validation strategies.

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 working on real modeling systems.
• Strong grounding in dynamical systems, time-series modeling, control, or sequence modeling.
• Experience evaluating models where ground truth is partial, delayed, or noisy.
• Ability to design validation strategies when standard benchmarks are insufficient.
• Comfort working with first-of-its-kind architectures and open-ended questions.

Responsibilities

• Validation of a new AI modeling primitive for long-horizon dynamical systems.
• Design of experiments and benchmarks that test stability, generalization, and predictive fidelity over time.
• Comparative evaluation against existing modeling approaches.
• Identification of failure modes, assumptions, and edge cases.
• Clear articulation of why the model succeeds or fails in different regimes.
• Translation of findings into guidance for future architecture and system design.
• Exploration of applicability across multiple domains with complex dynamics, not just biology

Skills

Dynamical systems

Time-series modeling

Model evaluation

Experiment design

Education

PhD or equivalent in relevant field

1+ year industry/applied research

Job description

Becoming in San Francisco seeks a Research Scientist to rigorously evaluate a new modeling primitive designed for complex, time-evolving systems. The role focuses on validating scope, limits, and evolution through careful analysis, experimentation, and comparison to existing approaches.

Your work will directly influence platform development and its application across domains with dynamic, long-horizon behavior, demanding clear articulation of failure modes and robust validation strategies.