P
PathAI
ML Engineer

Machine Learning Intern/Co-op

HybridJuniorML Engineerposted 2w ago
✦Role summaryAI-generated

The intern will work on developing and evaluating machine learning models for pathology image analysis, collaborating with the AI team to improve diagnostic accuracy. This role offers hands‑on experience with deep learning frameworks and real‑world clinical data in a remote‑friendly environment.

Skills required

About this role

PathAI's mission is to improve patient outcomes with AI-powered pathology.

Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials. Rigorous science and careful analysis is critical to the success of everything we do. Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes.

Our Intern/Co-op program is designed to give you a real-world view of what it's like to be a Machine Learning Engineer at PathAI. You will be embedded directly into our AI product and ML Core teams, working on active projects that have a direct impact on our technical roadmap and business goals.

You will collaborate with a cross-functional team of scientists, engineers, and product managers to translate clinical needs into scalable ML solutions. This is your chance to contribute to meaningful work that directly relates to patient outcomes.

Timeline: This opportunity is for Fall 2026 / Winter 2027. We are looking for interns/co-ops who can commit to 3-6 months at PathAI (4-6 months preferred).

Location: Boston, MA and New York, NY; potential for remote US based candidates.
The ability to be onsite 3 days a week is highly preferred.

What You’ll Do:

  • Contribute to the design, development, and evaluation of machine learning models for our AI products, AI services and core ML models.
  • Work on subprojects derived from our ongoing ML product roadmap, focusing on areas like model improvements with novel techniques.
  • Partner with our MLOps and platform teams to help build and improve our ML pipelines.
  • Participate in experimental design, analysis, and team knowledge-sharing initiatives like design reviews, technical forums and journal clubs.

Who You Are:

  • You are enrolled in a M.S., or Ph.D. in Computer Science, Electrical Engineering, Biomedical Engineering with a focus on Computer Vision and Machine Learning.
  • You have strong programming skills in Python and hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow).
  • You have a demonstrated level of understanding of the fundamentals of machine learning and/or computer vision.
  • You have excellent communication skills and a desire to work in a highly collaborative, cross-functional environment.
  • You have a passion to improve patient lives

Preferred:

  • You have previous experience working on complex open-source projects and any AI based products.
  • You have participated in student organization and/or other leadership activities
  • You have contributed to AI/ML publications.

The expected salary range for this position based on the primary location of Boston, MA is $55-70 per hour. Actual pay will be determined on experience, qualifications, geographic location, and other job-related factors permitted by law. This position also qualifies for paid holiday time off.

PathAI is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

✕ position closed

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