AI Engineer

Nucleo Research builds AI technology for oncology, turning routine medical imaging into quantitative insights for researchers, hospitals and pharma teams across cancer care and research. Nucleo’s goal is to develop the technologies that will enable the next generation of patient care and clinical trials.

Our founders are former researchers at Apple and Stanford. Healthcare data is messy and complex, but it holds real patterns that the right data and models can uncover. That’s the problem we started Nucleo to solve, and it’s what makes the work here both hard and worth it.

We are looking for exceptional candidates to join our technical team and build our platform engineering. They will help shape how an AI-first company works. The ideal candidate uses AI coding tools every day, takes full ownership of what they build, and will be central to how we scale. We value fast learners over years of experience. Strong candidates have owned products end to end across several layers of the stack (commercial work, side projects and open source all count) and are exceptional at working with AI coding tools: judging their output against a high bar, catching their errors, and giving feedback that cuts down on future corrections. A smaller part of the role is supporting and onboarding our users. They are comfortable collaborating across time zones and traveling for on-site integrations.

If interested, please answer a few additional questions at this link.

You might be a good fit if you

• Are motivated by improving patients' lives using AI
• Are comfortable with change, ambiguity and wearing multiple hats
• Have worked in a domain where constraints stack up, like regulation, user preferences and org politics (for example, healthcare or fintech)

Responsibilities

• Build and test features across Nucleo’s products
• Ship ML models and ensure accurate and optimized behavior in production
• Own and improve our release pipeline
• Enforce regulatory policies and track sign-offs
• Travel to customer sites for on-site integrations when needed
• Support our customers and help onboard new users

Your first 30/60/90 days

• 30 days: Get to know our CT and MRI pipeline from start to finish, ship your first change to production and run the evaluation for one of our segmentation models. Become familiar with our regulatory constraints.
• 60 days: Own one model or feature end to end: train or adapt it, validate it on held-out data, and present the results to the team and a customer. Become a key player in our regulatory pipeline.
• 90 days: Be the go-to person for that area: decide what we build next there, help on-site with a customer integration and make the pipeline measurably faster or more reliable. Become owner and maintainer of other existing features.

What we're looking for

• Experience in a technical field such as computer science, math or bioengineering, ideally related to AI or healthcare
• Proven end-to-end product ownership. Non-commercial projects or open-source contributions are also welcome
• Solid hands-on coding experience without LLM assistance, in any language (Python or TypeScript, the core languages of our stack, are a plus)
• Strong skills with AI coding tools, as described above

Nice to have

• A master's or PhD in a technical field
• Experience in regulated environments (healthcare, fintech) or at a startup
• Experience with on-prem integrations in regulated environments, like healthcare, labs, or biotech.
• Publications in AI, computer vision or AI for healthcare
• Experience communicating with non-technical stakeholders and working async

What you get

• Competitive compensation (reported compensation is based on the US market and will be adjusted based on location)
• Flexible hours. Because we collaborate across time zones, meetings and handoffs sometimes land outside typical working hours.
• A real say in shaping the product
• Dedicated travel and conference budget for professional development

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Interview Process

1. Screening call (15 min). An exploratory call to check fit based on the candidate's experience, background and interests. Candidates who pass receive a take-home assignment designed to take 1 to 2 hours, due within a week. It's discussed in the technical interview.
2. Technical interview (60 min)
   • Take-home discussion (20 min). We discuss the take-home assignment. AI use is encouraged, but candidates should be able to defend their design choices, referring to their submitted code, and explain the pros and cons of alternative approaches.
   • Algorithms and data structures (20 min). The candidate is given a LeetCode-style problem and talks us through a solution, without writing code.
   • Previous experience (20 min). A deep dive into past work and the lessons that carry over to the role at Nucleo.
3. Final interview with the co-founders (60 min). A higher-level discussion of team fit, motivation and alignment with Nucleo's mission, plus the candidate's questions about the company and role.