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EElucid

Software Engineering Intern: Spring 2027

Boston, MAListed 3h ago

  • Hybrid
  • Internship
  • Spring 2027
  • BS
  • MS
  • PhD

About this role

We are a rapidly growing, Boston-based medical technology company using AI to enable clinicians to make informed decisions on cardiovascular care - leading to better patient outcomes, improved quality of care, and reduced healthcare costs. Come work at Elucid (http://www.elucid.com/) and be part of delivering on our mission to prevent heart attacks and strokes!

This role is hybrid and based out of our Boston office, open to local students.

As our software engineering intern, you’ll work closely with a dedicated mentor and experienced engineers, gaining hands-on exposure to how a growing software-as-a-medical-device (SaMD) company builds, tests, and ships production software. You’ll write and review code, build internal tools and test automation, and use Claude Code to build Claude skills that automate repeatable work for the team. It’s a great way to get an inside look at engineering in MedTech while building skills that translate directly to a future career in software development.

This is a part-time internship (with co-op options for those who want more full-time hours) starting in January 2027, with at least two in-office days a week on Monday and Wednesday. Our sunny, spacious office is located in the heart of Boston, next to Boston Common.

This is a role for someone who is a:
- Curious builder who enjoys writing, testing, and debugging code, and who is eager to apply their coursework in a fast-paced, dynamic startup environment.
- Strong problem-solver with solid computer science fundamentals and a sharp eye for detail.
- Clear communicator who works well on a team, takes feedback gracefully, and can work independently when needed.
- Resourceful, proactive learner who enjoys finding more efficient ways to get work done, including with AI tools.
- Junior, senior, or co-op student pursuing a degree in Computer Science or a related field (e.g., Software Engineering, Computer Engineering, Data Science), with an interest in healthcare technology.
With these skills and experiences:
- Working knowledge of at least one of Python, C++, or TypeScript.
- Experience with GitHub or similar source control, including branching, committing, and opening pull requests.
- Comfort using AI tools such as Claude Code or Replit for coding and problem-solving, plus curiosity about how to use them effectively and responsibly.
- Previous project or internship experience (exposure to medical device, healthcare, or regulated software environments a plus).
- Interest in image processing, 3D medical imaging, and supporting machine learning teams with data (prior experience is a plus but not required)
Salary: $35-$40/hour for up to an estimated 20 hours a week
----------------------------------------------------

At Elucid, we believe the best work happens in a flexible hybrid environment. Boston-area employees are encouraged to work from the office on Mondays and Wednesdays, with the option to come in additional days if they prefer. Fully remote employees outside the Boston area come in as needed.

We are an equal opportunity employer and value diversity at Elucid. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability.

We understand that self-doubt can sometimes prevent great candidates from applying. If you believe you can make an impact at Elucid, even if you don’t meet every qualification, we encourage you to apply!

$35–40/hrListed pay
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Location
Boston, MA
Work model
Hybrid
Role type
Internship
Season
Spring 2027
Degrees
BS · MS · PhD
Family
Software Engineer
Listed
3h · Oct 30, 2026
E

Elucid

Series C · 51–200 employees

  • AI & Machine Learning
  • Healthcare & Health Tech

Elucid develops AI-powered medical imaging software that analyzes coronary CT angiography scans to quantify and characterize arterial plaque. Its PlaqueIQ platform supports cardiovascular risk assessment and personalized treatment decisions.

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