Senior Actuator Modeling Engineer

At Rhoda AI, we’re building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state-of-the-art foundation world models that control our robots. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling long-tail edge cases, made possible by our cutting edge research and end-to-end system design. We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality.

As a Modeling Engineer, you will develop the mathematical foundation that guides actuator design across our robot fleet. You translate the statistical population of real-world use cases our robots encounter into concrete, defensible design decisions: how big the motor should be, how the gear train should be sized, what to cool, what to optimize, and what each attribute is actually worth.

Actuators are the meeting point of electromagnetics, thermodynamics, structural mechanics, and tribology — and the design space is vast. Your models are how we navigate it. The simulations, duty cycles, and sensitivity analyses you build will directly shape what gets prototyped, what gets shipped, and what we choose to leave on the table.

We operate as T-shaped engineers. You must be a strong generalist across actuator physics, but your superpower for this role is building rigorous, data-driven models that turn ambiguous customer needs into prioritized, monetized design targets.

1D System Modeling: Develop and maintain the 1D actuator model that serves as the team's source of truth for performance prediction. Keep it correlated to bench and robot data, and evolve it as new physics, components, and use cases emerge.

Duty Cycle Development: Synthesize the statistical population of customer use cases into representative duty cycles that drive sizing, validation, and life prediction. Defend assumptions with data.

Optimization & Sensitivity Analysis: Build optimization models that identify the highest-leverage design changes. Define which attributes matter most based on the statistical distribution of what robots actually do in the field, and quantify the sensitivities that connect design parameters to system outcomes.

Attribute Monetization: Assign a data-driven monetary value to each actuator attribute (torque density, efficiency, mass, cost, life, thermal headroom, etc.) so trade-offs become rigorous business decisions rather than opinions.

Trade-Off Simulation: Develop simulations that inform actuator architecture trade-offs early — before tooling, before prototyping — and feed optimized targets back into the design team.

Thermal Modeling: Build thermal models that drive motor cooling decisions, from passive conduction paths to active cooling strategies, and correlate them against test data.

Fatigue & Life Modeling: Develop fatigue models for proper gear and bearing sizing, grounded in the duty cycles you've built. Connect B-life targets to design margin and validation strategy.

Efficiency Modeling: Build efficiency models that capture electromagnetic, mechanical, and thermal losses across the full operating envelope, and use them to guide both design and control strategy.

Cross-Functional Influence: Partner closely with actuator design, controls, reliability, and systems engineering. Your models are only valuable if they change decisions — make sure they do.

Education: Bachelor's degree in Mechanical Engineering, Electrical Engineering, Mechatronics, Applied Physics, or a related field. Advanced degree a plus.

Experience: 5+ years developing physics-based models for electromechanical systems, ideally actuators, motors, drivetrains, or robotics. At least one program where your modeling work materially shaped a hardware design.

Core Fundamentals: Strong grasp of electromechanical actuator physics — electromagnetics, gear mechanics, bearing life, heat transfer, and rotor dynamics — and the ability to know which physics matter for which question.

Technical "Superpower": Deep proficiency in 1D system modeling and multi-domain simulation. You can build a model that's just complex enough to answer the question and no more, and you know when a closed-form estimate beats a finite element study.

Statistical & Optimization Fluency: Comfort with duty cycle synthesis, design of experiments, sensitivity analysis, and formal optimization methods. You think in distributions, not point estimates.

Software: Proficiency in MATLAB/Simulink, Python (NumPy, SciPy, pandas), or equivalent. Familiarity with tools like Simcenter Amesim, GT-SUITE, Motor-CAD, or similar 1D/system simulation environments.

The "Owner" Mindset: First-principles thinking, strong written communication, and the judgment to know when a model is good enough to make a decision and when it isn't.

Reliability Engineering: Experience with Weibull analysis, B-life specification, and reliability-as-a-time-bound-probability framing. Familiarity with IEC 61508 or ISO 13849 (SIL/PL, PFD/PFH) for safety-critical actuator contexts.

Motor & Drivetrain Depth: Hands-on experience with PMSM/BLDC motor design, planetary or harmonic gear sizing, or bearing life modeling.

Thermal-Fluid Modeling: Background in CHT, lumped-parameter thermal networks, or motor cooling system design.

Data Pipeline Experience: Built systems that ingest field or test data and use it to continuously update model parameters.

Robotics or Automotive Background: Prior experience modeling actuators for legged robots, manipulators, EVs, or other high-cycle electromechanical applications.