Machine Learning Engineer

Machine Learning Engineer

Company:
Boston Human Capital Partners, Inc.

Location:

Cambridge, MA (On-site, Remote)

Type:
Full-time

Posted: 15 hours ago

An AI startup is seeking an experienced Machine Learning (ML) Engineer to help advance our mission of revolutionizing healthcare. The ML engineer will be responsible for the prototyping and deployment of state-of-the-art ML solutions as well as the improvement of the company's ML development pipeline.

• Develop state-of-the‑art computer vision models for breast cancer risk prediction.
• Continuously improve the company's ML development pipeline.
• Assist the team with data collection and infrastructure work.
• Conduct model validation in collaboration with academic and clinical partners.
• Provide engineering assistance for regulatory submissions.
• Write publications in peer‑reviewed literature and generate intellectual property materials.

• 5+ years of ML development work in a corporate setting in a fast‑paced environment.
• 5+ years of developing computer vision ML models (deep learning, image processing, large vision models) for image analysis, image segmentation and image classification tasks.
• Demonstrated innovations in ML and/or software as a medical device using ML.
• Practical experience with the following technologies:
  • ML architectures: CNN, Vision Transformers, large vision models.
  • ML toolkits:
    Tensor Flow, Keras, scikit‑learn, PyTorch.
  • Cloud: ML managed services, preferably on AWS.
  • ML:
    Tensor Flow, Sage Maker Pipelines.
  • Databases: data warehouse and relational databases.
  • Deployment:
    Docker containers, AWS Code Pipeline.
  • Pipeline orchestration: AWS Step Functions, Airflow, MLFlow.
  • Application exchange: REST API, JSON.
  • Programming languages:
    Python.
  • Software tools:
    Git, Git Hub, JIRA, Confluence.

• Corporate experience in the regulated medical imaging or healthcare IT industry.
• Prior experience implementing orchestration and data management workflow solutions.
• Understanding of the software development life cycle for medical device software.
• Experience in version control of ML models (code, data, config, model) and model registries.

• Undergraduate degree in Computer Science or Engineering. Master’s degree or PhD in computer science or engineering preferred.