Top Senior Machine Learning Jobs in NYC, NY

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

The Senior Lead Machine Learning Engineer will design, implement, and scale ML applications, ensuring performance and high availability while collaborating with cross-functional teams.

Own product strategy and roadmap for Roku's recommendations platform, drive experiments and KPI-driven decisions, collaborate with engineering, ML, data science, UX and business partners, and deliver measurable engagement and monetization outcomes across multiple surfaces.

Lead end-to-end data and ML infrastructure powering underwriting and enterprise data ontology. Own ingestion, large-scale model training, low-latency inference, backtesting, monitoring, and API serving. Manage and technical-lead a data engineering and data science team, partner with actuarial and product teams, and improve iteration speed and data quality across healthcare claims use cases.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

Design, develop, and deploy scalable AI/ML solutions and agentic LLM systems for payments. Build data pipelines, perform analysis, partner with Product, Technology, Risk and Compliance, track model performance and regulatory documentation, and translate model outcomes into business impact.

Develop, deploy, and maintain scalable AI/ML solutions for payments including fraud detection and customer experience. Build large-scale data pipelines and feature stores on AWS, fine-tune LLMs/transformers, run experiments, monitor model performance, ensure regulatory compliance, and translate model outcomes into business metrics while working cross-functionally.

Lead end-to-end delivery of production AI/ML capabilities for payments (fraud, anomaly detection, automation, routing). Design and deploy neural/Transformer models, optimize for latency/scale/cost, define evaluation and monitoring, ensure model governance and audit readiness, collaborate with Product/Engineering/Risk, and mentor teams while communicating trade-offs to senior stakeholders.

Lead design, build, deploy, and maintain production ML models and pipelines at scale. Collaborate with product and data science teams to architect cloud-based ML systems, automate CI/CD, monitor and retrain models, ensure model governance and explainability, and drive high availability and performance.

Lead product strategy for AI-powered personalization and recommendation capabilities. Define requirements, partner with Data Science and Engineering to build, test, launch and optimize models, monitor performance, run experiments, prioritize enhancements using analytics, and communicate results to stakeholders while ensuring scalability, compliance and strong customer experiences.

Lead product efforts for machine learning-driven marketing and acquisition products. Collaborate with data scientists, engineers, and designers to define acceptance criteria, break down epics into MVP features and user stories, prioritize roadmaps, manage agile delivery, and drive stakeholder alignment to build scalable ML product platforms.

Designs, deploys, validates, and operationalizes AI/ML solutions for infrastructure reliability, forecasting, observability, automation, and enterprise decision-making. Builds models, NLP capabilities, data pipelines, APIs, dashboards, and MLOps workflows across hybrid cloud and on-premises environments. Manages the model lifecycle, including governance, validation, monitoring, remediation, and production support. Partners with engineering, operations, risk, cybersecurity, architecture, data science, and product teams while communicating technical outcomes and risks to technical and executive stakeholders.

Design, build, and productionize core AI systems for document understanding, forecasting, and agentic workflows. Handle messy data ingestion, evaluation, monitoring, anomaly detection, entity reconciliation, and backtesting to deliver reliable, product-focused ML solutions.

Lead ML engineer responsible for architecting, building, deploying, and operating production-scale machine learning systems. Collaborate with product and data science teams to design models, data pipelines, CI/CD, and cloud architectures; retrain and monitor models; ensure performance, reliability, governance, and explainability; and mentor or lead engineering teams.

As a Senior Machine Learning Engineer, you will lead model validation for AI systems, challenge model soundness, and build validation tools for high-stakes areas such as credit and fraud prevention.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

The Lead Machine Learning Engineer designs and implements ML applications, builds data pipelines, collaborates with teams, and ensures performance and governance of ML systems.

Design, implement, and deploy large-scale machine learning and optimization algorithms for ad ranking. Build and evaluate models, create online/offline evaluation frameworks, partner with product to define objectives and constraints, and communicate results to stakeholders to drive ad quality and performance.

Lead and grow a team of ML and software engineers to build large-scale indexing, retrieval, and ranking systems for Snapchat ads. Define architecture and evaluation frameworks, collaborate with product and data science, measure outcomes, and raise engineering and ML standards while shaping Snap's broader ML strategy.

Design, build, and productionize causal ML models (uplift, heterogeneous treatment effects), analyze A/B tests and quasi-experiments, collaborate with product and engineering, ensure scalable, maintainable infrastructure, and balance model complexity with interpretability and business impact.

Build and ship end-to-end ML/AI systems (traditional ML, NLP, LLMs/AI agents) on transaction, merchant, and location data. Prototype, productionize, and monitor models, collaborate with engineering and product, and (for senior hires) shape roadmap and mentor teammates.

Leads and manages an applied machine learning team developing fraud detection and identity verification models. Responsibilities include people management, coaching, code review, production ML systems, model lifecycle ownership, experimentation, deployment, monitoring, fraud research, cross-functional prioritization, and communicating results. The role remains hands-on while partnering with Product, Engineering, Risk, and leadership to drive measurable business impact and shape future product strategy using machine learning and AI.

Design and build full-stack features for an LLM-powered coaching platform: architect backend services and APIs, integrate models, craft responsive front-end experiences, ensure enterprise-scale performance and security, drive CI/CD and observability, debug across the stack, and mentor engineers while collaborating with product and design.

Design, prototype, and productionize AI systems (recommendations, natural language interfaces, agents). Partner with product and engineering to identify AI opportunities, build scalable ML infrastructure, and manage the full ML lifecycle from data curation through training, deployment, and monitoring.

Lead product strategy and execution for AI-driven expert research: translate investor needs into AI workflows, define roadmap, run user research and experiments, collaborate with engineering/design, and drive launches while optimizing accuracy and reliability of LLM/ML outputs.