Included Health AI Engineer interview questions & guide 2026

The questions below represent the core competencies tested at Included Health. While specific technical tasks may shift, the focus remains on your ability to apply AI theory to real-world infrastructure and clinical data.

Generative AI & NLP

• How would you architect a RAG pipeline to minimize hallucinations when retrieving clinical documentation?
• What are the trade-offs between different embedding models for semantic search in a healthcare context?
• How do you evaluate the output quality of an LLM in an environment where ground truth is subjective?

Preparation at Included Health should be grounded in the practical application of your skills. You are expected to demonstrate not only theoretical mastery but also the ability to reason through the constraints of a production environment.

System Design Proficiency – You will be evaluated on your ability to build scalable, resilient AI systems. Focus on the trade-offs between latency, cost, and accuracy, especially when dealing with high-volume clinical data.

Technical Communication – The ability to articulate complex AI concepts to cross-functional partners is essential. Practice explaining why a specific architecture or model choice is the right fit for a business problem.

Clinical/Domain Awareness – While you do not need to be a clinician, showing an understanding of data privacy, accuracy, and the high-stakes nature of healthcare is a significant advantage.

Ownership and Problem-Solving – Interviewers look for candidates who take ownership of the full lifecycle of a model. Be prepared to discuss how you handle failure states, monitoring, and iterative improvements.

The interview process at Included Health is designed to assess both your deep technical expertise and your ability to work within a collaborative, mission-driven team. You will generally progress from an initial screen to a series of technical deep-dives that cover coding, system design, and behavioral fit. The pace is deliberate, focusing on depth rather than breadth.

This timeline provides a high-level view of your progression from initial contact to the final decision. Use this structure to calibrate your preparation, ensuring you have enough time to review both your coding fundamentals and your past project experiences. Remember that the process is designed to be a conversation, not an interrogation; engage with your interviewers to understand the specific challenges their teams are currently facing.

RAG and Embeddings

This area tests your ability to build information retrieval systems that are accurate and performant. You should understand the mechanics of vector databases and how to optimize retrieval for domain-specific language.

Be ready to go over:

• Vector search strategies and indexing trade-offs.
• RAG pipeline design including chunking strategies and re-ranking.

As an AI Engineer, you will be responsible for the end-to-end development of AI-driven features. You will work closely with product managers to define requirements, then translate those into scalable architectures. You will spend significant time refining data pipelines, optimizing inference latency, and ensuring that the outputs of your models meet the high standards of quality required in a healthcare setting.

Collaboration is constant. You will interface with data engineers to ensure high-quality data ingestion, with backend engineers to integrate AI models into production services, and with clinical leads to validate model outputs. Success in this role is measured by your ability to ship reliable, high-impact AI solutions that improve the lives of our members.

A successful candidate possesses a strong foundation in software engineering, coupled with specialized expertise in modern AI frameworks.

• Must-have skills: Proficient in Python, experience with modern LLM frameworks (e.g., LangChain, LlamaIndex), deep understanding of vector databases, and experience designing scalable APIs.
• Nice-to-have skills: Experience with cloud-based ML infrastructure (AWS/GCP), expertise in MLOps, and prior experience in healthcare or highly regulated industries.
• Experience: A track record of deploying AI models into production environments is preferred.

Q: How difficult are the coding rounds? A: The coding rounds are calibrated to assess your ability to write clean, maintainable code under time pressure. They focus on practical engineering problems related to data processing and system optimization rather than obscure algorithmic puzzles.

Q: What is the best way to prepare for the system design round? A: Focus on real-world constraints. Think about how you would design a system that needs to be both highly available and compliant with data privacy regulations.

Q: How long does the process take? A: While it varies, most candidates move through the loop within a few weeks. Consistency and responsiveness help keep the process moving efficiently.

Q: Is this role fully remote? A: Yes, the AI Engineer position is remote-friendly, allowing you to contribute from various locations while staying connected to your team.

• Show Your Thought Process: In system design, the "why" is more important than the "what." Explain your trade-offs clearly.
• Focus on Reliability: In healthcare, accuracy is paramount. Always mention how you validate your models.
• Understand the Business: Research Included Health’s mission. Connecting your technical decisions to member outcomes will set you apart.

The AI Engineer role at Included Health offers a unique opportunity to build technology that directly improves patient outcomes. By mastering the core pillars of RAG pipelines, LLM evaluation, and system design, you will be well-positioned to succeed in your interview loop. Remember that your ability to bridge the gap between complex AI systems and real-world clinical needs is your greatest asset.

You can explore additional interview insights, practice questions, and preparation resources on Dataford. We encourage you to review your project portfolio and practice articulating your technical decisions with clarity and confidence.

The compensation data provided reflects the current market range for this role. Candidates should interpret these figures as a starting point, recognizing that final offers are determined by a combination of years of experience, specialized technical expertise, and internal leveling.