Research Scientist, Dynamical Systems & AI

About Becoming

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.

The Role

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.

What You’ll Own

• 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
  
  

Who You Are

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

Required

• PhD or equivalent experience 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
  
  

Strong Signals

• Experience with state-space models, world models, neural ODEs, diffusion over time, or hybrid approaches
• Prior work on systems where prediction degrades over long horizons
• Exposure to biological, physical, or other real-world dynamical systems
• Track record of identifying why models fail — not just improving metrics

• Competitive salary and meaningful equity
• Full benefits
• High-trust, high-ownership environment
• Rapid growth in scope and responsibility