Top Senior Machine Learning Jobs in NYC, NY

Design, develop, test, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for scalability, cost, latency, throughput, and hardware utilization. Collaborate with engineers, scientists, technical program managers, and product managers while shaping the technical vision and roadmap for responsible AI platforms.

Lead the Ranking & Relevance team of software and machine-learning engineers. Own retrieval, ranking, recommendation, and personalization systems that match patients with therapists. Drive the transition from filter-based matching to outcome-aware machine learning, establish shared marketplace objectives, improve experimentation and model monitoring, and build evaluation and drift-detection capabilities. Hire and develop engineers, set technical standards, and partner with product, data science, payer, and provider engineering teams in a sensitive healthcare domain.

Design, build, deploy, scale, and monitor production machine learning systems. Develop models, application code, data pipelines, cloud architectures, automated tests, and CI/CD processes. Collaborate with Product and Data Science teams, ensure high availability and performance, govern models for risk and responsible AI, and lead teams developing ML solutions.

Lead the design, development, deployment, and support of scalable AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Develop optimization techniques to improve AI system scalability, latency, throughput, hardware utilization, and cost. Shape foundational AI roadmaps, collaborate with cross-functional teams, and mentor engineers while applying emerging research to production.

Leads the design, development, deployment, and maintenance of machine learning systems at scale. Responsibilities include building ML models and data pipelines, writing production code, optimizing cloud architectures, automating testing and deployment, monitoring and retraining models, and ensuring performance, governance, security, responsible AI, and explainability. The role collaborates with Product and Data Science teams in an Agile environment focused on enterprise AI coding tools.

Design and productionize causal machine learning models, including uplift modeling and treatment-effect estimation. Analyze A/B tests and quasi-experiments, develop experimentation strategies, evaluate modeling tradeoffs, and build scalable infrastructure. Collaborate with product and engineering teams, conduct code reviews, maintain engineering standards, communicate technical insights, and mentor others.

Design, research, and benchmark machine learning algorithms for anomaly detection, error outlier analysis, and faulty deployment analysis. Build, deploy, scale, and monitor user-facing ML features, models, services, and infrastructure in production. Analyze high-volume streaming data, communicate complex algorithms to nontechnical audiences, participate in research journal discussions, maintain team-owned systems, and join the on-call rotation while partnering with engineering and product teams.

Design, train, evaluate, and productionize machine learning models (including embeddings and foundation models) on large-scale data. Partner with product and engineering to run experiments, improve customer experiences, and deploy scalable solutions using Spark and AWS.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Lead product vision and execution for a portfolio of ML-powered personalization and insights products. Translate research and analytics into roadmaps, run experiments, partner with data science and engineering, ensure privacy/fairness, enable self-serve platforms, and measure outcomes across marketing, servicing, assistants, and financial management.

Review and audit machine learning models and model risk practices across the enterprise. Partner with cross-functional teams to design, train, evaluate, validate, and implement ML and NLP solutions, using tools like Python, Spark, H2O, and AWS. Translate technical findings into business recommendations and assurance for stakeholders and the Audit Committee, while researching and applying emerging Generative AI and state-of-the-art methods.

Leads the design, development, and productionization of machine learning systems at scale. Builds and validates models, data pipelines, cloud-based ML architectures, and application code; automates testing and deployment; monitors and retrains production models; and ensures high availability, performance, security, governance, and responsible AI practices. Collaborates with Product, Data Science, and Agile engineering teams to deliver optimized ML applications.

Build and validate ML, optimization, and statistical models on large structured and unstructured consumer datasets; generate insights and actionable recommendations; communicate results to senior stakeholders; contribute across the full modeling lifecycle and advance team knowledge.

Lead development and deployment of optimization and machine learning models to improve retail banking decisions. Translate business problems into mathematical formulations, build production-grade optimization and simulation models, collaborate with Product and Engineering to integrate solutions, and run end-to-end model development, evaluation, validation, and implementation.

Lead forensic technology engagements by designing and deploying machine learning models, statistical algorithms, and data pipelines for digital investigations and electronic discovery. Collaborate with clients to define goals, analyze cloud and enterprise data, develop reusable automation components, communicate findings to executive audiences, and deliver training. The role also involves managing people on high-profile consulting engagements and providing client-facing technical leadership.

Contribute to machine learning model design, training, and evaluation for fraud detection. Communicate findings, develop metrics, and support production ML workflows while ensuring compliance and model governance.

Design, build, deploy, scale, and maintain production machine learning systems and models. Lead technical design, model and application development, data pipeline construction, cloud architecture, testing, CI/CD, monitoring, governance, and responsible AI practices. Collaborate with Product, Data Science, and Agile engineering teams while leading teams developing enterprise AI coding tools.

Serve as a technical partner to sales and customer success teams, advising high-value customers on cloud infrastructure and AI/ML solutions. Responsibilities include architecture reviews, proofs of concept, demos, onboarding, technical workshops, infrastructure optimization, LLM deployment and fine-tuning, GPU workload optimization, AI application design, data pipeline and database integration, escalations, documentation, and development of tools for premium customers. Collaborate globally with Engineering, Product, Support, and account teams.

Leads the design, development, deployment, and operation of scalable machine learning systems. Builds ML models, data pipelines, cloud architectures, and production applications; optimizes training and performance; automates testing and deployment; monitors and retrains models; and applies responsible AI, explainability, governance, and security practices. Collaborates with Product, Data Science, and Agile engineering teams while providing technical and potentially people leadership.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Lead and grow a research engineering team developing conversational AI agents. Own the technical roadmap across model post-training, evaluation, safety, speech, and agent behavior; convert research ideas into measurable production programs; review architecture and experiments; ensure rigorous, reproducible engineering; and partner with infrastructure, platform, product, and customer teams to deploy reliable AI systems.

Leads the development and ownership of Cedar’s production machine learning personalization engine. Responsibilities include ML modeling, MLOps, feature pipelines, deployment, inference, monitoring, feedback-loop optimization, compliance and fairness guardrails, technical leadership, code reviews, cross-functional collaboration, and management of ML engineers. The role requires hands-on contributions to scalable ML systems serving millions of healthcare transactions while guiding roadmap execution and model improvement.

Lead analytics and ML work to drive Aetna Medicare growth: design experiments, segmentation, forecasting, and end-to-end ML system architecture; prototype models, collaborate with engineering and product teams for production deployment, and communicate data-driven recommendations to cross-functional stakeholders.

Lead development of large-scale, low-latency ML systems for ad serving to optimize pricing, allocation, and auctions. Design experiments, build production ML pipelines and model serving, partner cross-functionally, and provide technical leadership and mentorship.

Owner of product features for Card Intelligence ML initiatives; collaborate with data scientists, engineers, and stakeholders to define roadmaps, break down epics into MVPs, prioritize features, and accelerate model-to-market delivery for fraud prevention.