Carrum Health: Staff Applied AI Product Engineer
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Description
Headquarters: Remote, USA
At Carrum, we are transforming how we pay for, deliver and experience healthcare. If you are passionate about changing healthcare and want to finally get rid of surprise bills, poor quality, and high prices, while thriving in an entrepreneurial, cutting-edge environment, we would love to connect with you.
In 2014 Carrum reinvented the Centers of Excellence (COE) category in digital health. Today, 95% of the US population lives within 50 miles of a Carrum COE and our providers rank in the top 10% nationally. Our team’s execution has been recognized by the venture community and we’ve raised more than $96M in aggregate from investors like OMERS, Tiger Global Management and Wildcat Ventures. Our impact has been externally proven in a 2021 RAND Corporation study and featured as a Harvard Business School (HBS) case study.
As a Staff Applied AI Product Engineer, you will bridge the gap between cutting-edge AI capabilities and real-world patient needs. You will take a hands-on leadership role in architecting and integrating LLM-driven features into our core platform. While our foundation is built on Ruby on Rails (SOA) and React, you will lead the evolution of our stack to support AI-native workflows and build agents to support key business initiatives such as improving care navigation quality and efficiency.
You will work closely with Data and Product teams (including a dedicated Applied AI PM) to determine use cases that require LLMs rather than ML, build robust RAG (Retrieval-Augmented Generation) pipelines, select appropriate foundation models, iterate on prompts, and design the "glue" that turns raw model intelligence into reliable product features. You will partner with our DevOps team to leverage AWS/Azure for simple and cost-effective cloud infrastructure deployment, ensuring that systems are both performant and resilient. A key responsibility will be implementing comprehensive monitoring and observability practices to guarantee the high availability, security, and scalability of our new AI services. Additionally, you will collaborate directly with our internal clinical experts to validate model outputs, ensuring your AI agents are safe, accurate, and truly helpful in a healthcare setting.
As a senior technical leader, you will own the AI engineering strategy, moving us beyond "demos" into scalable, production-ready systems. You will establish patterns for building “smart products,” model evaluation, latency optimization, and cost management, while ensuring we maintain strict data privacy and HIPAA compliance. You will champion Responsible AI by operationalizing the internal guidelines set by our AI Council, ensuring that fairness checks, bias detection, and safety guardrails are strictly implemented to serve all patient demographics equitably. You will mentor the wider engineering team on AI-native development practices (such as prompt engineering and context management) and collaborate with product managers to identify high-leverage opportunities where AI can radically simplify the healthcare experience. You will partner with the VP of Engineering and Chief Product Officer to staff and align delivery teams on your initiatives, while working with product teams to define project timelines and milestones from inception to go-live.
This is a full time position, the salary range for this role is $190,000 - $260,000 depending on level of experience and geographic location.
You’re excited about this opportunity because you will...
- Define the Gen AI technical roadmap: You will build the foundation of Applied AI function at Carrum and have a direct impact on how Carrum leverages Generative AI to automate complex healthcare coordination and improve patient outcomes.
- Build "magic" features: Move fast to prototype, iterate and ship AI-powered experiences that feel magical to users, such as instant answer bots or automated appointment logistics.
- Architect for the future: Be the sole expert on integrating vector databases, orchestration frameworks (like LangChain), and LLM APIs into a mature Service-Oriented Architecture.
- Bridge the gap: Act as the translator between the "stochastic" world of AI models and the "deterministic" world of software engineering, ensuring reliability and trust.
- Lead without ego: Mentor talented full-stack engineers on how to incorporate AI tools into their workflows, lifting the technical ceiling of the entire team.
- Own the outcome: Take ownership of t
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Interview Prep Guide
Preparation Strategy
To prepare for this role, review your experience with LLMs, AI-native workflows, and system design. Practice describing your approach to prompt engineering, context management, and ensuring data privacy and HIPAA compliance. Be prepared to discuss your experience collaborating with cross-functional teams and ensuring fairness and equity in AI systems. Additionally, review your knowledge of Ruby on Rails, React, and cloud infrastructure deployment using AWS/Azure.
Likely Interview Rounds
- 1. Technical~60 min
What to prep: Review Ruby on Rails, React, and LLM-driven feature integration. Practice designing RAG pipelines and implementing monitoring and observability practices.
- How would you architect an LLM-driven feature into our core platform?
- What experience do you have with Ruby on Rails and React?
- Can you describe your approach to building robust RAG pipelines?
- How do you ensure the high availability, security, and scalability of AI services?
- 2. System design~60 min
What to prep: Review system design principles for scalable AI systems. Practice designing cloud infrastructure deployments and ensuring data privacy and HIPAA compliance.
- How would you design a scalable AI system for healthcare?
- Can you describe your experience with cloud infrastructure deployment using AWS/Azure?
- How do you ensure data privacy and HIPAA compliance in AI systems?
- 3. Behavioral~60 min
What to prep: Review your experience working with clinical experts and ensuring fairness and equity in AI systems. Practice describing your experience collaborating with cross-functional teams.
- Can you describe a time when you had to validate model outputs with clinical experts?
- How do you ensure fairness and equity in AI systems?
- Can you tell me about a project where you had to collaborate with cross-functional teams to deliver an AI-driven product?
Most Likely Questions
- What is your experience with LLMs and AI-native workflows?
- Can you describe your approach to prompt engineering and context management?
- How do you stay up-to-date with the latest developments in AI and machine learning?
- Can you describe a time when you had to troubleshoot an issue with an AI system?
- How do you ensure the reliability and maintainability of AI systems?
Common Pitfalls
- Lack of experience with LLMs and AI-native workflows
- Inability to communicate complex technical concepts to non-technical stakeholders
- Insufficient attention to data privacy and HIPAA compliance
- Failure to ensure fairness and equity in AI systems
Free Prep Resources
- • LeetCode
- • System Design Primer (GitHub: donnemartin)
- • NeetCode
- • AWS Cloud Practitioner Essentials
Salary Negotiation Tips
Given the salary range of $190,000 - $260,000, be prepared to negotiate based on your level of experience and geographic location. Consider highlighting your relevant experience, skills, and achievements to support your desired salary.