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We are a robotics startup in the Bay Area working on the physics of contact: measuring what happens when a robot touches an object, modeling it numerically, and validating those models against instrumented hardware. Our work sits between tactile sensor hardware, physics simulation, and the learning systems that consume both.
The role
You will own the physics modeling and simulation side of our stack: the numerical models that turn raw tactile sensor signals into physical quantities, the simulation models that reproduce those signals from contact geometry and material behavior, and the experimental procedures that show where the two agree and where they do not. You inherit working code, documentation, a metrology bench with a reference instrument, and open technical questions.
Were looking for someone who is comfortable asking: What is physically happening at the contact interface, how should we model it numerically, and how do we prove the model agrees with real hardware?
What you'll do
Build and maintain numerical models that convert tactile sensor signals into force, deformation, and contact-state estimates
Design a numerical solver that is capable of dealing with nonsmooth events such as frictional contact
Develop the simulation path: model the sensor's compliant surface and its interaction with the scene in a soft-body solver, fit fast surrogate models
Write sensor and material characterization procedures for metrology bench: design and run indentation, shear, and slip protocols against the reference instrument.
Quantify the gap between simulation and hardware under controlled protocols, and reduce it where the physics allows.
Define acceptance criteria and recalibration triggers from drift, hysteresis, and temp sensitivity.
Write clear technical documentation: equations, parameter ranges, fitting procedures etc.
Requirements
MS in mechanical engineering, applied physics, applied mathematics, computational mechanics or robotics. PhD preferred.
Strong numerical analysis background: discretization, stability, conditioning, convergence, and error analysis. You can explain why a scheme is stable and what happens when it is not.
Strong working knowledge of hyperelasticity, viscoelasticity, and friction/contact models.
Hands-on experience with physics solvers: soft-body, finite element, or contact/rigid-body simulation. You have implemented or substantially modified a solver, not only used one.
Strong continuum and contact mechanics fundamentals: stress, strain, large deformation, compliant contact.
Parameter identification against instrument data: least-squares or Bayesian fitting, error budgets, uncertainty propagation.
Numerical coding in Python (NumPy, JAX, or similar) with attention to reproducibility and testability.
