Senior Machine Learning Engineer, CAD Computational Design

The Company

We partner with a well-funded, fast-scaling healthtech startup reinventing musculoskeletal care using proprietary AI and 3D-printing technology.

Signify's mission is to empower every person, regardless of their background or circumstances, with an equitable chance to achieve the careers they deserve. Building a diverse future, one placement at a time. Check out our DE&I page here

Role and 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 into new device categories such as orthotics and prosthetics.
• Work with the AI team to define the interface between learned components, such as landmark estimation and parameter prediction, and the rule-based CAD layer.
• Develop geometric tooling including freeform surfaces, trimlines, top-surface estimation, offsets, and feature placement to produce clinically correct, manufacturable geometry.
• Drive geometry programmatically through CAD and geometry APIs and kernels, moving beyond GUI-based workflows toward automated, high-throughput modeling.
• Own the bridge from design to manufacturing, ensuring outputs are printable and meet quality requirements, and help automate design QC.

Required Skills

• 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 working through a GUI, using tools such as Rhino/Grasshopper, Onshape API, SolidWorks API, Fusion API, or similar Solid.
• Proficiency in Python, TypeScript, C++, or another programming language.
• A collaborative, translational mindset, comfortable working with clinicians and designers to turn expert intuition into precise, parameterized systems.
• Ability to work well in an early-stage, fast-moving environment where the problem space is still being defined.
• Experience with geometric modeling kernels such as OpenCascade or Parasolid.
• Experience with implicit geometric representations.
• Experience with simulation-in-the-loop design, shape optimization, or topology optimization.
• Familiarity with CAD interoperability standards such as STEP, IGES, or JT.
• Background in footwear, orthotics, prosthetics, dental, medical devices, or another domain that maps anatomy to custom physical products.