Senior Machine Learning Engineer, CAD Computational Design

What You'll Do

• Design and build parametric, procedural CAD pipelines that generate custom orthopedic devices from anatomical landmarks and clinical parameters.
• Partner with clinicians and design experts to extract domain knowledge and translate it into explicit parameter spaces, constraints, and rules that can be automated.
• Build a maintainable library of parametric components and design primitives that generalize across products and extend cleanly into new device categories (e.g., AFOs and other O&P devices).
• Collaborate with the AI team to define the interface between learned components (landmark estimation, parameter prediction) and the rule-based CAD layer.
• Develop geometric tooling — freeform surfaces, trimlines, top-surface estimation, offsets, and feature placement — that produces clinically correct, manufacturable geometry.
• Drive geometry programmatically through CAD/geometry APIs and kernels, moving beyond GUI-based workflows toward high-throughput, automated modeling.
• Own the bridge from design to manufacturing, ensuring outputs are printable and meet quality requirements, and helping automate design QC.

What You'll Bring

Required

• 5+ years building parametric and procedural CAD systems, ideally in a product or manufacturing context.
• Strong programmatic CAD experience — scripting and automating geometry rather than driving a GUI (e.g., Rhino/Grasshopper, Onshape API, SolidWorks API, Fusion API, or similar).
• Solid command of geometric modeling fundamentals: NURBS, B-Rep, meshing, and surface/solid operations.
• Proficiency in Python, Typescript, C++ or any software programming language.
• A collaborative, translational mindset — comfortable sitting with non-engineers (clinicians, designers) and turning fuzzy expert intuition into precise, parameterized systems.
• Ability to thrive in an early-stage, fast-moving environment where the problem space is still being defined.

Nice to Have

• Experience with geometric modeling kernels (e.g., OpenCascade, Parasolid).
• Experience with implicit geometric representations.
• Experience with simulation-in-the-loop design, shape optimization, or topology optimization.
• Familiarity with CAD interoperability standards (STEP, IGES, JT, or similar).
• Exposure to AI-driven or generative CAD workflows — enough to collaborate effectively with our AI team.
• Background in footwear, orthotics, prosthetics, dental, medical devices, or another domain that maps anatomy to custom physical products.

Compensation

Competitive salary plus meaningful early-stage equity and benefits. (Range ~$190,000–$270,000 depending on experience and level.)