Senior AI Engineer - Clinical Reasoning

Senior AI Engineer

Location: New York City

Work Style: Hybrid Onsite

Employment Type: Full-time

Focus: AI, LLMs, Clinical Reasoning, Evaluation, Retrieval, Applied ML

About Our Client

Our client is building AI technology with the mission of making high-quality healthcare more accessible, affordable, and scalable.

Their AI-powered clinical platform already supports millions of patient consultations, and the company is working toward scaling that impact significantly while continuing to improve clinical safety, reasoning quality, accuracy, and trust.

This is an opportunity to join a team building real-world clinical AI systems used by patients every day. The company operates in a live healthcare environment, giving the team a unique dataset and feedback loop to test, improve, and deploy AI systems in practical clinical care.

About the Role

Our client is hiring a Senior AI Engineer to help build the next generation of clinical AI systems.

This role blends research and engineering. The ideal candidate is not just running experiments or writing notebooks — they are building real systems that reason, retrieve evidence, evaluate performance, and improve over time.

You’ll work on agentic reasoning, retrieval, evaluation infrastructure, model learning, and clinical decision support systems. The goal is to help every component of the AI platform become safer, more accurate, more useful, and more trustworthy with each iteration.

What You’ll Do

• Design and build agentic clinical reasoning systems
• Develop AI architectures that support reasoning, reflection, verification, tool use, routing, uncertainty handling, and escalation
• Build systems where specialized agents and models work together to support safe and reliable clinical decisions
• Create evaluation platforms, rubrics, simulations, and experiments to measure AI performance in clinical use cases
• Identify whether improvements should come from reasoning, retrieval, model behavior, data, or engineering changes
• Apply methods such as fine-tuning, distillation, reinforcement learning, preference optimization, and prompt or system optimization
• Build training data, feedback, reward, and experimentation pipelines
• Develop search, ranking, retrieval, and grounding algorithms tied to trusted medical evidence and patient context
• Improve retrieval systems based on their impact on downstream clinical decisions, not just document relevance
• Own problems end-to-end, from framing and experimentation through shipping and measurement
• Collaborate closely with engineering, clinical, product, and physician-scientist partners

What We’re Looking For

• Strong experience building real AI, ML, or LLM-powered systems
• Deep experience in at least two of the following areas:
• Agentic architectures, reasoning systems, and tool use
• Model evaluation, experimentation, and rubric design
• Model training, fine-tuning, distillation, or reinforcement learning
• Search, ranking, retrieval, RAG, or grounding systems
• Strong ML fundamentals, including training data, objectives, metrics, failure analysis, calibration, and validation
• Ability to work in complex domains where ground truth is incomplete and expert opinions may differ
• Strong engineering fundamentals with experience building production systems, not just research prototypes
• Clear communication and strong collaboration across engineering, clinical, and product teams
• Ability to think through business impact and prioritize technical work accordingly
• Comfort operating with autonomy in a builder-first environment

Experience Profile

Successful candidates will typically have one of the following backgrounds:

• Advanced degree in a quantitative, computational, scientific, or related discipline with 3+ years of highly relevant applied AI/ML or research experience
• 7+ years of relevant experience building and researching ML systems
• Recent hands-on work with LLMs, generative AI, agentic systems, retrieval systems, or production AI infrastructure

Our client cares more about the depth and quality of your work than a specific credential or traditional career path.

Bonus Experience

• Published research, patents, meaningful open-source contributions, or novel production ML systems
• Experience building AI systems at an early-stage or high-growth company
• Experience in healthcare, clinical AI, regulated industries, or safety-critical environments
• Familiarity with clinical workflo…