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

Build and ship production algorithmic systems for healthcare, including machine learning, optimization, heuristics, and expert systems. Responsibilities include framing complex problems, defining objectives and metrics, validating solutions, deploying models, iterating based on outcomes, reviewing code and analyses, and communicating findings to stakeholders. Example projects include provider tiering optimization and member engagement models using claims and behavioral data.

Own the reliability, performance, observability, and cost-efficiency of production machine learning systems. Architect Garner’s ML platform, including feature stores, model registries, model-serving infrastructure, and CI/CD pipelines. Establish automated data-quality, model-validation, drift-monitoring, and incident-response processes. Set technical direction, develop platform standards and KPIs, and prepare for future continuous-training capabilities while collaborating with ML, data, platform, and product engineering teams.

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).

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.