Job type: Permanent
Salary: $150K – $230K + Equity
Role Location: Boston, United States
The Company:
We’re partnering with a well-funded, fast-scaling healthtech startup reinventing musculoskeletal care using proprietary AI and 3D-printing technology.
The business has developed a web-based, sensor-free AI vision platform that transforms a 30-second scan into a clinically approved, precision-manufactured medical device. Already trusted by Fortune 50 employers, major health systems, and national manufacturers, the platform is expanding access to preventative care for hundreds of thousands of Americans.
Fresh off a stealth funding round led by top-tier VCs, the company is:
- Growing 10x year-over-year
- Profitable month over month
- Scaling rapidly toward $100M ARR
- Operating with a high-ownership, execution-first culture out of Boston
This is a rare opportunity to join at a true inflection point - early enough to shape the company, late enough to have real traction.
Role and Responsibilities:We’re hiring a Senior Machine Learning Engineer to help build the core AI systems behind a next-generation healthcare platform that turns smartphone video into clinically accurate 3D models of human anatomy.
You will own the pipeline that bridges raw computer vision data and physical 3D-printed medical solutions, transforming noisy real-world scans into precise, CAD-compatible models used to improve patient outcomes.
This role sits at the intersection of machine learning, computer graphics, biomechanics, and real-world manufacturing.
You’ll work closely with engineers, researchers, and product leaders to design systems that translate cutting-edge ML research into reliable production technology used in healthcare.
Job Requirements:3D Reconstruction & Generative Vision
Strong experience building models for 3D reconstruction from video or images, including approaches such as:
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Signed Distance Fields (SDFs)
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Neural Radiance Fields (NeRFs)
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Gaussian Splatting
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Mesh-based reconstruction
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Point cloud processing
You understand the computational trade-offs between different 3D representations and how to apply them in real-world systems.
Deep Learning & Computer Vision
- Deep experience designing and training deep learning models for complex visual data, including non-Euclidean data structures.
- You are comfortable building pipelines that extract meaningful structure from noisy, real-world video and imaging data.
- Strong proficiency in Python and PyTorch is required.
CAD & Geometry Processing
- Experience working with computational geometry, CAD systems, or mesh processing pipelines.
- You can bridge the gap between AI inference outputs and parametric CAD models that can be directly used in manufacturing and 3D printing workflows.
Production Machine Learning
- Experience deploying machine learning systems in production environments.
- You are comfortable working with ML infrastructure on AWS, including model deployment, scalable inference pipelines, and optimizing performance for real-world usage.
- Experience with SageMaker, Lambda, or serverless ML systems is a plus.
Rapid Prototyping & Research Implementation
- You stay up to date with the latest research and can quickly translate new advances into working prototypes.
- You move fast, experiment frequently, and turn research ideas into practical systems.
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