Top AI & Machine Learning Jobs in New York City, NY

Lead Garner Health Intelligence to build and operate doctor-ranking and provider analytics. Own technical direction, product roadmap, production systems, team hiring/development, and business outcomes. Collaborate with Product, Engineering, Clinical, and Commercial teams to ensure algorithms improve cost and quality and drive commercial performance.

Design, ship, and iterate production algorithmic systems for healthcare problems (ML, optimization, heuristics, or hybrid). Frame objectives, define metrics, validate solutions, fine-tune LLM products, and monitor outcomes while collaborating with stakeholders.

Design, develop, and productionize algorithmic systems (ML, optimization, heuristics, LLMs) for healthcare decisions. Own end-to-end solutions: problem framing, objective definition, model selection, shipping, evaluation, and monitoring to improve real-world outcomes and business metrics.

Lead and grow an applied science squad that owns end-to-end production algorithms. Set technical direction, prototype and ship ML/optimization/LLM solutions, define success metrics, validate performance, and partner with product, engineering, and business stakeholders to improve clinical and cost outcomes.

Lead and deliver end-to-end production algorithmic systems for healthcare products: frame problems, define objectives, select ML/optimization/heuristic approaches, ship solutions, and measure real-world outcomes. Act as a hands-on individual contributor and player‑coach, technically leading and mentoring a small Applied Science team while reviewing methods company-wide and advancing high-impact initiatives (provider tiering, LLM-based care, member engagement models).

The Senior Applied Scientist will develop and optimize algorithms for evaluating healthcare providers, focusing on quality, cost, and access, and will work closely with engineering to implement scalable solutions.

Own and architect Garner's production ML platform, ensure reliability, performance, cost-efficiency, implement ML CI/CD, drift monitoring, observability, and set standards for future MLOps team.

Design, build, and own algorithmic systems that evaluate providers, make recommendations, and optimize outcomes across cost, quality, and access. Define objective functions and metrics, run experiments, deploy scalable production models, and iterate based on real-world healthcare outcomes while collaborating closely with engineering and business stakeholders.