Mathematical Scientist: Stochastic Models and Risk Quantification

Overview

Field AI is transforming how robots interact with the real world. We build risk-aware, reliable, field-ready AI systems that address complex robotics challenges, combining stochastic analysis, differential equations, and uncertainty quantification to produce explainable, robust AI for real-world deployment in Dull, Dirty, and Dangerous (DDD) environments.

We seek a mathematician specializing in stochastic differential equations (SDEs), uncertainty quantification, and risk-aware decision-making to drive first-principles AI innovation in robotics. This role is foundational to our mission, developing new mathematical paradigms that govern autonomy in the real world and ensure explainability, robustness, and safety at every level of deployment.

• Develop stochastic models for real-time risk quantification and uncertainty propagation in robotics foundation models
• Apply Fokker-Planck (Kolmogorov forward) equations
  , Hamilton-Jacobi-Bellman PDEs
  , and stochastic optimal control to develop explainable and physics-grounded foundation models
• Develop novel stochastic inference frameworks
  , leveraging score-based generative models
  , neural stochastic differential equations (SDEs) for uncertainty-aware perception, state estimation, and trajectory forecasting
• Work on large deviations theory
  , stochastic stability
  , and rare-event simulation to model robot behavior under extreme environmental uncertainty
• Build probabilistic programming and variational inference frameworks enabling robots to adapt dynamically to unseen conditions
• Collaborate with AI and engineering teams to translate mathematical insights into real-time robotics intelligence and operational decision-making
• Publish research in stochastic control, risk-sensitive reinforcement learning, and uncertainty-aware AI to shape the next era of explainable autonomy

• Ph.D. in Mathematics, Applied Mathematics, Theoretical Physics or a related field with focus on stochastic processes, PDEs, or dynamical systems
• Deep expertise in stochastic calculus
  , measure-theoretic probability
  , and functional analysis
  , with applications to uncertainty quantification and risk-aware control
• Experience in Hamilton-Jacobi PDEs
  , path-integral control
  , and entropy-regularized control
• Proficiency in high-performance computing & optimization for solving high-dimensional SDEs and PDEs at large scales (e.g., via spectral methods
  , GPU-based Monte Carlo,
  Galerkin method
  )
• Strong programming skills in Python, C++, or Julia
  , with experience in numerical libraries such as Py Torch ,
  JAX
  , or Tensor Flow
• Knowledge of Bayesian inference
  , information-theoretic decision-making
  , and probabilistic programming

• Experience integrating mathematical models into real-world robotics applications is a strong plus

Our salary range is generous ($70,000 - $200,000 annual), with final pay depending on geographic location, background, and experience. We welcome hybrid or remote work options in addition to on-site collaboration.

We are solving one of the world’s most complex challenges: deploying robots in unstructured, unknown environments. Our Field Foundational Models set a new standard in perception, planning, localization, and manipulation, with an emphasis on explainability and safety.

You will work with a world-class team that values creativity, resilience, and bold thinking. Our experience spans organizations such as Deep Mind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and Space

X.

We seek innovators who challenge the status quo, explore uncharted territory, and bring interdisciplinary expertise. Our team includes top AI talent, software developers, engineers, designers, field deployment experts, and strong communicators.

We are headquartered in Mission Viejo, CA, with US-based and global teammates.

We celebrate diversity and are committed to an inclusive environment. Candidates are evaluated based on merit, qualifications, and performance, without discrimination on race, color, gender, national origin, ethnicity, veteran status, disability, age, sexual orientation, gender identity, marital status, or any other legally protected status.

• Not Applicable

• Full-time

• Research, Analyst, and Information Technology

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