Research Scientist, Dynamical Systems & AI

Becoming Research Scientist

Becoming is building Developmental Intelligence: AI for predicting how organisms change over time.

Most 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.

We 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.

We are hiring a Research Scientist to help verify, validate, and shape a new modeling primitive designed for complex, time-evolving systems.

This 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.

Your work will directly influence how the platform develops and how broadly it can be applied.

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

You are someone who:

Operates with high agency — you identify problems, define solutions, and execute

Brings high energy to complex, ambiguous engineering challenges

Acts with high integrity — you are honest about tradeoffs, risks, and failure modes

Communicates directly and clearly, especially when something won't work

Is self-aware about your strengths and gaps, and proactively fills them

Thinks naturally in terms of dynamics, stability, and generalization

Enjoys stress-testing models to understand their limits

Is comfortable working on foundational problems with ambiguous answers