Senior Applied Research AI Engineer

Senior Applied Research AI EngineerLocation: New York CityWork Style: Hybrid OnsiteEmployment Type: Full-timeFocus: AI, LLMs, Clinical Reasoning, Evaluation, Retrieval, Applied MLAbout Our ClientOur 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 RoleOur 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 DoDesign and build agentic clinical reasoning systemsDevelop AI architectures that support reasoning, reflection, verification, tool use, routing, uncertainty handling, and escalationBuild systems where specialized agents and models work together to support safe and reliable clinical decisionsCreate evaluation platforms, rubrics, simulations, and experiments to measure AI performance in clinical use casesIdentify whether improvements should come from reasoning, retrieval, model behavior, data, or engineering changesApply methods such as fine-tuning, distillation, reinforcement learning, preference optimization, and prompt or system optimizationBuild training data, feedback, reward, and experimentation pipelinesDevelop search, ranking, retrieval, and grounding algorithms tied to trusted medical evidence and patient contextImprove retrieval systems based on their impact on downstream clinical decisions, not just document relevanceOwn problems end-to-end, from framing and experimentation through shipping and measurementCollaborate closely with engineering, clinical, product, and physician-scientist partnersWhat We’re Looking ForStrong experience building real AI, ML, or LLM-powered systemsDeep experience in at least two of the following areas:Agentic architectures, reasoning systems, and tool useModel evaluation, experimentation, and rubric designModel training, fine-tuning, distillation, or reinforcement learningSearch, ranking, retrieval, RAG, or grounding systemsStrong ML fundamentals, including training data, objectives, metrics, failure analysis, calibration, and validationAbility to work in complex domains where ground truth is incomplete and expert opinions may differStrong engineering fundamentals with experience building production systems, not just research prototypesClear communication and strong collaboration across engineering, clinical, and product teamsAbility to think through business impact and prioritize technical work accordinglyComfort operating with autonomy in a builder-first environmentExperience ProfileSuccessful 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 experience7+ years of relevant experience building and researching ML systemsRecent hands-on work with LLMs, generative AI, agentic systems, retrieval systems, or production AI infrastructureOur client cares more about the depth and quality of your work than a specific credential or traditional career path.Bonus ExperiencePublished research, patents, meaningful open-source contributions, or novel production ML systemsExperience building AI systems at an early-stage or high-growth companyExperience in healthcare, clinical AI, regulated industries, or safety-critical environmentsFamiliarity with clinical workflows, healthcare data, HIPAA, FHIR, EHR systems, or HL7Experience with human-feedback systems, RLHF, simulations, or synthetic data generationExperience with AI safety, bias detection, calibration, fairness, or model reliabilityWhy This OpportunityBuild AI systems that can meaningfully improve access to healthcareWork on real clinical AI problems with real patient usage and feedbackJoin a team focused on reasoning, retrieval, evaluation, safety, and trustWork side by side with physician-scientists and experienced technical buildersOwn high-impact AI systems end-to-endOperate with autonomy in a fast-moving, builder-first environmentContribute to technology designed to scale from millions of consultations to much larger clinical impactCompensation & BenefitsCompetitive salaryMeaningful equity with upside as the company growsComprehensive health benefitsHigh autonomy and ownership over important technical problemsOpportunity to build AI systems transforming healthcare at scaleIdeal Candidate ProfileThe 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.