Principal Mechanical Systems Simulation Engineer

4MP is hiring a Principal Engineer to own mechanical systems simulation for its precision manufacturing platform. The role derives and implements physics-based solvers, characterizes materials, validates predictions against measurements, and integrates production simulation software with the broader engineering platform.

Responsibilities:

• Model Structural Response Under Load: Treat the component as a structural element - think a loaded shaft - and predict deflection and load-induced positional error as a function of size, geometry, and material properties
• Characterize Material Behavior: Derive stiffness, damping, and wear/loss characteristics from real test data, and build models that generalize to untested materials and geometries
• Parameterize and Sweep at Scale: Build the tooling that drives simulations across component size, material, operating speed, and load and engagement conditions. The combinatorics are enormous; the software must cover that space efficiently rather than replicate individual tests
• Build the Simulation Data Pipeline: Produce and manage large, well-structured simulation datasets - schemas, provenance, reproducibility - and define the sampling strategy that makes the resulting library most useful downstream
• Validate Against Measurement: Compare predictions to real test and metrology data, and iterate until they are quantitatively reliable - with stated uncertainty, not qualitative agreement
• Write the Solvers: Derive the governing equations and implement them yourself in production code - discretization, linear algebra, integration, optimization. Commercial packages are references and cross-checks here, not the deliverable
• Build Production Software: Write real code, not throwaway scripts: modular and tested against analytical and experimental benchmarks, reviewed, packaged, and documented well enough for teammates to use and extend
• Engineer for Performance: Profile and optimize the hot paths - vectorization, parallelism, compiled extensions, GPU where it pays - so that sweeps of thousands of permutations run in hours, not weeks
• Work Within a Cutting-Edge Simulation Team: Sit alongside the engineers who own optical sensing and experimental validation, and computational geometry and sensor simulation - you own the mechanical systems simulation domain and the interfaces to theirs, separating structural effects from sensing effects
• Integrate With the Platform: Work closely with the software, geometry, and sensing engineers so your models plug into the broader codebase and follow shared conventions - not standalone scripts on your own machine
• Shape the Simulation Roadmap: In coordination with the rest of the team, help decide which components, materials, and conditions to sample, and where to build versus buy
• Share Depth With the Team: Mentor colleagues on the mechanics and the numerics, review their models, and document your assumptions so others can build on and challenge your work

Requirements:

• 10+ Years of Experience: Deep, hands-on background in structural mechanics, materials characterization, or physics-based simulation of real mechanical systems
• Principal-Level Ownership: A track record of owning a technical domain end-to-end - scoping ambiguous problems, choosing the approach, and being the person the team relies on for the answer
• Professional Software Development: Years of writing and shipping production code in a shared codebase - version control, testing, code review, CI, packaging, and debugging software other people depend on. This is a requirement, not a nice-to-have
• Physics-First Modeling: Demonstrated ability to derive governing mechanics, implement them in code, and validate against measurement - rather than operating a simulation package as a black box
• Solid Mechanics Depth: Beam and structural deflection theory, elastic-plastic deformation under load, and material constitutive behavior
• Material Characterization: Experience deriving stiffness, damping, and wear behavior from test data and generalizing it across geometries and operating conditions
• Numerical Methods in Code: Implementing numerical methods yourself - discretization, linear algebra, time integration, root finding, optimization - with judgment about stability, conditioning, and convergence
• Collaborative Engineering: Experience working inside a shared team codebase - integrating against interfaces owned by others, following common conventions, and writing code that teammates can pick up and extend
• Performance Engineering: Profiling and optimizing numerical code - vectorization, memory layout, parallel and multi-core execution, compiled extensions such as C++, Cython, Rust, or Fortran, and GPU or CUDA where warranted
• Python at Depth: Expert-level Python and the scientific stack - NumPy, SciPy, and the surrounding tooling - not scripting-level familiarity. Comfort orchestrating large parameter sweeps and managing the resulting data
• Validation Discipline: A track record of closing the loop between simulation and physical measurement, including quantifying model error
• Educational Foundation: Master's or PhD in Mechanical Engineering, Engineering Mechanics, Physics, Applied Mathematics, or a related field - or a Bachelor's with equivalent depth of hands-on modeling experience
• Applicants without hands-on experience modeling physical systems and validating those models against measurement will not be considered
• Published or Patented Work: Peer-reviewed publications, patents, or open-source contributions in structural mechanics, materials modeling, or simulation methods
• Surrogate Modeling: Building fast reduced-order or surrogate models that stand in for expensive simulation inside a production loop
• Rotordynamics and Vibration: Dynamic response of rotating shafts and components, including damping, modal behavior, and stability
• Thermal-Structural Coupling: Machine-structure effects such as thermal growth and its interaction with structural deflection
• Geometry Parameterization: Component-geometry parameterization - size, shape, mass distribution - and its effect on deflection and structural response
• FEA Fluency as Background: Working knowledge of commercial FEA - enough to use it as an independent cross-check on your own solvers. Valued as background depth, not as the job
• Open-Source Scientific Software: Contributions to or maintenance of a scientific computing library, solver, or simulation framework used by people outside your own team
• Compute Infrastructure: Running simulation workloads on clusters or cloud - job orchestration, containerization, and cost-aware scaling
• Industrial Context: Familiarity with machine tools, industrial equipment, process controls, or precision metrology
• Startup Experience: Comfort building in an agile, ambiguous environment where the playbook does not exist yet and the first version has to work
• Technical Influence: Experience being the voice of the physics in the room - with engineering leadership, customers, or national laboratory partners
• Ownership Drive: You are not looking for a title; you want to own a physics foundation that a new category of manufacturing is built on