Top Senior Machine Learning Jobs in NYC, NY

Develop and deploy AI/ML models for consumer banking use cases including lead scoring, next-best-action, retention, cross-sell, and customer prioritization. Define success metrics, analyze complex datasets, communicate recommendations to nontechnical stakeholders, and partner with product, technology, governance, risk, and operations teams. Ensure models meet regulatory and model risk standards through documentation, monitoring, drift detection, and retraining.

Develop and deploy advanced AI and machine learning models for branch network optimization, resource allocation, sales effectiveness, and workforce performance. Work with geospatial, demographic, transactional, and behavioral data; collaborate with business, governance, UX, and technology stakeholders; translate model results into business recommendations; and support regulatory model reviews. Apply techniques including spatial and graph-based modeling, reinforcement learning, optimization, and behavioral modeling.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Own the architecture, deployment, performance, and reliability of Stickerbox’s production machine learning pipeline. Optimize latency, cost, batching, caching, GPU utilization, model serving, and inference across image generation, speech recognition, and character models. Lead deployment strategies, capacity planning, observability, safety enforcement, automated quality checks, migrations, release engineering, and on-call operations for a global consumer hardware launch.

Build and scale AI/ML solutions for healthcare revenue cycle automation. The role owns AI outcomes, identifies high-value use cases, shapes technical and product roadmaps, converts prototypes into scalable production systems, and architects infrastructure for training, testing, monitoring, security, and deployment. The engineer will apply ML, NLP, and LLM approaches to claims and workflow optimization while balancing technical tradeoffs, business value, cost, and operational efficiency.

Designs, trains, evaluates, and operationalizes intelligent document-processing solutions using OCR, machine learning, LLMs, Python, and cloud data pipelines. Builds extraction, validation, reconciliation, exception-handling, and human-review workflows for sensitive information. Develops secure, auditable, configuration-driven systems with API integrations, monitoring, testing, CI/CD, and infrastructure as code. Partners with business experts to define document fields, quality thresholds, validation rules, and acceptance criteria.

Lead the design, development, deployment, and operation of machine learning models and platforms at scale. Build distributed ML infrastructure, data pipelines, cloud-based architectures, and production services. Develop and test application code, automate deployment, monitor and retrain models, and apply CI/CD, governance, security, and responsible AI practices. Collaborate with Product, Data Science, and Agile teams to solve complex business problems.

Build and maintain scalable machine learning infrastructure, including distributed GPU training pipelines, model promotion workflows, monitoring integrations, and ML frameworks. Optimize training throughput, support model deployment and observability, and collaborate with product, architecture, data science, and engineering teams. The role requires strong Python, cloud, ML framework, big data, training and inference system, and enterprise design experience, with preferred expertise in recommendation systems, CUDA, containers, orchestration, and data storage.

Lead the technical direction and implementation of Current’s machine learning platform, covering feature pipelines, training data, model training, serving, monitoring, reproducibility, and production-readiness standards. Establish scalable workflows, improve training-serving consistency, support data scientists, define model governance contracts, evaluate build-versus-buy tooling, and drive adoption across engineering, data science, risk, marketing, and finance.

Develop statistical and machine learning techniques, quantitative models, and scalable automation for evaluation pipelines. Build data-processing infrastructure, improve evaluation efficiency, and solve ambiguous problems across statistics, ML, and large-scale systems. Collaborate with engineers, data scientists, product managers, and leadership while maintaining code quality in a complex codebase.

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.

The Senior Research Engineer will develop and deliver scalable ML solutions, collaborate with teams, and apply innovative technologies in AI to impact professionals in legal and accounting fields.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include designing ML systems, developing data pipelines, operating distributed systems and cloud infrastructure, automating testing and deployment, monitoring and retraining models, and applying responsible AI practices. The role collaborates with Product and Data Science teams and uses Python, Java, Scala, or related languages.

Own and lead the development, migration, and operation of recommendation systems for Fable and Everand. Responsibilities include moving Fable’s recommender to the Orion ML platform, extending ranking and retrieval capabilities, incorporating social signals, managing production quality and reliability, designing A/B tests, partnering with product managers, leading cross-team delivery, and improving engineering practices.

Build and maintain scalable machine learning models and end-to-end ML pipelines for logistics automation. Responsibilities include data exploration, model deployment and monitoring, production infrastructure improvements, collaboration with engineering and product teams, code reviews, architectural guidance, and technical mentorship.

Own and improve Quora’s second-phase ranking models for feed and digest experiences. Build end-to-end machine learning systems covering data pipelines, candidate extraction, feature engineering, model training, and production integration. Identify new product applications for machine learning, develop efficient algorithms with other ML engineers, and contribute methodologies for large-scale recommendation systems.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Build, deploy, scale, and maintain machine learning models and platforms in production. Develop optimized data pipelines, distributed ML systems, cloud-based architectures, and automated CI/CD workflows. Collaborate with product and data science teams, monitor and retrain models, apply responsible AI practices, and ensure secure, governed software delivery. The role requires extensive experience with ML frameworks, distributed systems, cloud services, Kubernetes, and production machine learning operations.

Morgan Stanley seeks a Machine Learning Researcher to solve critical problems with innovative research. Responsibilities include applying machine learning techniques and strong communication skills within a collaborative team. Ideal candidates have 4+ years of research experience and proficiency in statistical learning methods.

Leads Hightouch’s machine learning organization, including product development, technical roadmapping, recruiting, reliability, and people management. The role focuses on personalization, product recommendations, probabilistic identity resolution, LLM-powered content generation, and scalable machine learning infrastructure for model training and inference.

Design and implement production agentic AI systems, RAG pipelines, intelligent document processing services, embedding and vector retrieval workflows, and multi-agent extraction systems. Build FastAPI and Java/Spring Boot services, databases, evaluation harnesses, and governance controls. Own major AI platform components, collaborate with architecture and business teams, and mentor mid-level engineers while driving technical excellence and production quality.

Lead design and delivery of end-to-end ML solutions for Wealth Management, build and deploy scalable models (recommender systems, GNNs, LLMs), run large experiments, validate and monitor production models, partner with cross-functional teams, and mentor data scientists to drive measurable business outcomes.

Lead the architecture and production operation of Grubhub’s ranking, recommendation, retrieval, and query-understanding systems. Drive improvements in long-term diner value through deep learning, embeddings, LLMs, experimentation, and real-time inference. Establish model evaluation, deployment, observability, and reliability standards across teams. Partner with Product, Search, Ads, and Data Platform, mentor engineers, influence technical strategy, and communicate trade-offs to executive stakeholders.