Simulation Engineer

Role Description

You will lead the development and evolution of the physical simulation layer used to represent deformable and partially rigid robotic systems. This includes responsibility for the underlying modeling approach, numerical methods, and software architecture. You will ensure simulations are accurate, efficient, and grounded in experimental reality, while producing outputs that integrate cleanly with optimization, learning, and analysis pipelines. Collaboration across disciplines is expected, but ownership of the simulation stack and its technical direction sits with this role.

Core Responsibilities

• Build and run detailed physical simulations that capture 3D deformation, material response, and dynamic behavior in soft and hybrid robotic systems.
• Enable simulation-driven design workflows, where physics models directly guide exploration, comparison, and decision-making.
• Establish validation and calibration pipelines that systematically reduce discrepancies between simulated and observed behavior.

Technical Scope

• Physics-based modeling
• Implement numerical models for large-deformation mechanics, nonlinear materials, and contact, with extensions to fluid or environmental interaction when relevant.
• Develop finite-element or reduced-order representations that expose interpretable physical quantities (e.g., strain, stress, energy, efficiency, task-level metrics).
• Convert raw simulation results into structured, machine-consumable outputs for downstream automation.
• Closed-loop simulation workflows
• Embed simulation into automated optimization and learning loops to support parameter tuning, design synthesis, and model refinement.
• Support autonomous experimentation workflows that use simulation to propose, test, and update designs.
• Define and maintain clear interfaces between simulation components and the rest of the system, including data formats and provenance.
• Performance and scale
• Balance numerical accuracy with runtime performance to support iterative and high-throughput use cases.
• Apply adaptive discretization, reduced-order modeling, or surrogate techniques to accelerate exploration.
• Support parallel and batch execution for large design sweeps or sensitivity analyses.
• Validation and uncertainty
• Design comparison frameworks that evaluate simulation predictions against physical measurements.
• Implement automated parameter identification and calibration methods.
• Characterize uncertainty, sensitivity, and known failure modes of models used in downstream decisions.
• Software development practices
• Build maintainable, well-documented APIs that expose simulation capabilities and results.
• Ensure reproducibility through logging, versioning, and experiment tracking.
• Contribute to shared codebases using modern development workflows, testing, and continuous integration.

Background and Experience

• Advanced degree (MS or PhD) in mechanical engineering, robotics, applied physics, computational mechanics, or a related field.
• Strong grounding in continuum mechanics, numerical methods, and finite element analysis.
• Practical experience with commercial or open-source multiphysics simulation tools.
• Proficiency in Python and C++ for scientific computing and simulation infrastructure.
• Experience diagnosing numerical stability, convergence, and meshing issues.
• Comfort working in collaborative, research-oriented engineering teams.

Preferred

• Prior work on soft, compliant, or hybrid robotic systems.
• Familiarity with robotics or physics simulation platforms (e.g., PhysX, Isaac Sim, MuJoCo, PyBullet).
• Experience connecting simulation outputs to optimization or learning methods.
• Hands-on involvement in experimental testing or model validation.
• Experience with scientific visualization tools (e.g., ParaView, VTK, PyVista, Blender).
• Exposure to CI/CD practices for research or simulation software.

What to Expect

• Work on technically deep problems at the intersection of mechanics, computation, and robotics.
• Ownership over a critical technical system and its future direction.
• Opportunities to grow into senior technical or leadership roles.
• A collaborative environment that values rigor, experimentation, and iteration.