Senior Applied Research AI Engineer

About the role

Senior Applied Research 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 workflows, healthcare data, HIPAA, FHIR, EHR systems, or HL7

Experience with human-feedback systems, RLHF, simulations, or synthetic data generation

Experience with AI safety, bias detection, calibration, fairness, or model reliability

Why This Opportunity

Build AI systems that can meaningfully improve access to healthcare

Work on real clinical AI problems with real patient usage and feedback

Join a team focused on reasoning, retrieval, evaluation, safety, and trust

Work side by side with physician-scientists and experienced technical builders

Own high-impact AI systems end-to-end

Operate with autonomy in a fast-moving, builder-first environment

Contribute to technology designed to scale from millions of consultations to much larger clinical impact

Compensation & Benefits

Competitive salary

Meaningful equity with upside as the company grows

Comprehensive health benefits

High autonomy and ownership over important technical problems

Opportunity to build AI systems transforming healthcare at scale

Ideal Candidate Profile

The ideal candidate is a research-minded AI engineer who wants to build intelligent systems that work in the real world. They care deeply about reasoning quality, evaluation, safety, and measurable improvement — and they have the engineering ability to turn ambitious ideas into production systems.

This person is excited by the challenge of building clinical AI that can earn trust over time.