Sr. Staff Software Engineer, Image AI and Modeling

Reposted 20 Days Ago
Carlsbad, CA
In-Office
170K-200K Annually
Senior level
Artificial Intelligence • Healthtech • Analytics
The Role
Lead the strategy and execution of Carlsmed's imaging AI initiatives, focusing on developing segmentation and 3D reconstruction pipelines for medical imaging data.
Summary Generated by Built In

Position Description

Reporting to the Vice President, Surgical Planning Technologies, the Sr. Staff Software Engineer, Image AI and Modeling, leads the strategy, execution, and clinical validation of Carlsmed’s imaging AI initiatives, focused on transforming raw medical scans into high-fidelity, clinically accurate 3D models. This role guides development across multiple imaging modalities (CT, MRI, X-ray), ensures regulatory compliance, and spearheads future capabilities in multi-modal model fusion.

Key Responsibilities

  • Own development of AI-based segmentation and 3D reconstruction pipelines for CT, MRI, X-Ray, and multi-modal data.
  •  Design and lead model retraining workflows within a regulatory framework (PCCP, 510(k)).
  •  Build and curate annotated datasets for spine imaging (CT,MRI, & X-Ray).
  •  Drive adoption of self-supervised, cross-modal, and generative AI techniques.
  •  Collaborate with clinical and product development teams to validate anatomical accuracy.
  •  Coordinate model performance evaluation, golden dataset strategy, and toolchain development.
  •  Collaborate with consultants, ML engineers and imaging scientists.

Qualifications

  • Ph.D. or M.S. in Biomedical Engineering, Machine Learning, Computer Vision, or related field.
  • At least 8+ years in software development, including 5+ years focused on building deep learning models for medical imaging applications (segmentation, registration, 3D reconstruction), using Python-based frameworks.
  •  Demonstrated proficiency with Python programming and scripting, particularly for data preprocessing, model development, and workflow automation.
  •  Solid background in navigating regulatory environments, including FDA 510(k) and PCCP submissions.
  •  Deep familiarity with spinal anatomy or orthopedic imaging workflows is highly desirable.
  •  Proven track record leading cross-functional initiatives, collaborating across clinical, regulatory, and engineering teams.
  •  Hands-on involvement with synthetic imaging techniques, including GANs and diffusion models.
  •  Strong foundation in multi-modal learning and anatomical registration.
  •  Well-versed in the FDA’s Good Machine Learning Practice (GMLP) principles.

Equal Opportunity Employer

Carlsmed is an equal opportunity employer.  All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.  Carlsmed is committed to providing reasonable accommodation for candidates with disabilities in our recruitment process. If you need any assistance or accommodation due to a disability, please let us know.

                                                                                                       

Compensation

We are pleased to provide a competitive salary and benefits. Our benefits reflect our investment in the overall health and well-being of our employees and their families. including paying 100% of monthly healthcare, dental and vision insurance premiums, a 401(k) plan with employer matching, and unlimited PTO. The expected pay range is: $170,000 - $200,000 annually plus a bonus opportunity. Compensation may vary based on related skills, experience, and relevant key attributes.

Top Skills

AI
Computer Vision
Deep Learning
Machine Learning
Python
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The Company
San Diego, CA
91 Employees
Year Founded: 2008

What We Do

Carlsmed is a commercial-stage medical technology leader and creator of the personalized spine surgery market with pioneering technology that is transforming spine surgery. The Company has developed the cutting-edge aprevo® Technology Platform that personalizes surgical procedures through a combination of proprietary AI-driven software and patient-specific fusion devices.

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