Senior Machine Learning Engineer, CAD Computational Design

Hike Medical•San Francisco, CA

1d•$190,000 - $270,000•Onsite

About The Position

Hike Medical is reinventing how custom orthopedic products are made, starting from a smartphone video to reconstruct a 3D model of a person's foot and create a custom-manufactured insole. They are a Series A company expanding into broader orthotic and prosthetic (O&P) devices. This role focuses on the Computational Design / CAD aspect, specifically the parametric design layer. The core of the job involves translating expert clinical knowledge into parametric systems by working with clinicians and design experts to define parameter spaces, constraints, and encoding them into automated CAD pipelines. The company is moving towards a CAD-driven system with AI on top, where AI predicts parameters rather than generating geometry end-to-end. The engineer will collaborate with AI, clinical, design, and manufacturing teams to build systems that reliably, flexibly, and at scale turn reconstructed feet and clinical requirements into manufacturable, custom devices.

Requirements

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 Haves

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 (deep ML expertise is not required; we have that side covered).
Background in footwear, orthotics, prosthetics, dental, medical devices, or another domain that maps anatomy to custom physical products.

Responsibilities

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.