Senior Machine Learning Engineer

Hike Medical CoBoston, MA
2d

About The Position

As a Senior Machine Learning Engineer, you will bridge the gap between raw computer vision and physical, 3D-printed reality. You will own the pipeline that translates a smartphone video into a high-fidelity, CAD-compatible 3D reconstruction of human anatomy.

Requirements

  • PhD in Machine Learning, Computer Graphics, Geometry Processing, or a related field, or equivalent experience.
  • Deep expertise in 3D representations (Meshes, Point Clouds, Voxels, SDFs) and their respective computational tradeoffs.
  • Proven track record in Generative Vision, 3D Reconstruction, or related topics.
  • Strong proficiency in Python and PyTorch.

Nice To Haves

  • Experience in CAD and computational geometry is a big plus.
  • Experience with ML infrastructure in AWS (SageMaker, Lambda, etc) and model deployment is a plus.
  • Experience in Biomedical AI or Biomechanics (e.g., modeling joint movement or tissue) is highly valued but not required.
  • Fast Iteration Mindset: Willingness to move fast, iterate confidently in ambiguous tasks and deliver results from imprecise instructions or requirements.
  • Product Minded: Our AI team communicates the art of the possible. Having a good eye for high-value product opportunities is a major plus.
  • Polymathic: You can discuss loss functions with researchers, API latency with engineers, and 3D printing constraints with manufacturing.
  • Strong Communicator: You clearly explain your ideas, challenge assumptions, and collaborate across engineering, AI, and product teams.
  • Mission-Driven: You want to build technology that helps people walk without pain and revolutionize the future of orthopaedic care.
  • Thrives in Collaboration: You push back, ask tough questions, and elevate the team.

Responsibilities

  • 3D Reconstruction & Generation: Develop and deploy SOTA models for 3D human body part reconstruction (SDFs, NeRFs, Gaussian Splatting, or Mesh-based approaches) from videos.
  • Deep Learning: Architect pipelines that handle non-Euclidean data to extract precise clinical measurements from noisy real-world scans.
  • CAD Integration: Bridge the gap between AI inference and parametric CAD modeling for automated 3D printing preparation.
  • Rapid Prototyping & Continual Learning: You should be used to keeping up to date on latest advances and quickly implementing these advancements into our systems.
  • Production ML: Work with our engineering team to deploy models into our AWS/serverless environment, ensuring clinical-grade accuracy at scale.
© 2024 Teal Labs, Inc
Privacy PolicyTerms of Service