Top Senior Machine Learning Engineer Jobs in NYC, NY

Design, implement, and maintain production-oriented NLP and LLM models; build data pipelines and HPC analyses; integrate ML solutions with clinical research tools; ensure data quality, privacy compliance, and collaborate across multidisciplinary teams.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

Responsible for managing the AI/ML project lifecycle, developing low-latency ad-serving platforms, and enhancing search solutions through advanced modeling techniques.

As a Senior Machine Learning Engineer in Model Risk Management, you will evaluate and validate models that assess risk in lending and fraud, ensuring their robustness and reliability. You will collaborate with model owners, tackle model challenges, and build evaluation tools critical for understanding model performance.

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 Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

Design, build, deploy, and maintain production ML systems and data pipelines at scale. Lead cross-functional teams to implement ML architectures, ensure model performance and governance, automate CI/CD for models, and leverage cloud platforms and ML frameworks to deliver reliable, explainable solutions.

Design, build, deploy, and maintain production-scale ML models and pipelines. Collaborate with cross-functional Agile teams to implement cloud-based architectures, optimize model performance, automate CI/CD, and ensure model governance, monitoring, and responsible AI practices.

Lead the development of machine learning algorithms for personalization and recommendations in Disney's streaming products while collaborating with product and engineering teams.

As a Senior AI/ML Engineer, you will manage the AI/ML lifecycle, develop predictive models, conduct anomaly detection, and integrate AI capabilities into the Confido platform.

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

The role involves designing and implementing production-grade machine learning systems for conversational AI experiences, focusing on user engagement and context management.

Build and improve large-scale machine learning models powering predictive aviation products, including runway landing, gate arrival, and taxi duration predictions. Work with hundreds of millions of historical data examples and complex data types, optimizing model performance and efficiency. Collaborate within an agile team, contribute original ideas, and develop solutions for open-ended aviation intelligence challenges.

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.

Build and scale production machine learning infrastructure, including data and training pipelines, deployment workflows, monitoring, and supporting systems. Partner with researchers and ML practitioners to productionize models, improve reliability, scalability, observability, reproducibility, speed, and cost efficiency, and automate operational processes across the ML lifecycle.

Build and scale production machine learning systems for personalization and marketing optimization. Design end-to-end AI, reinforcement learning, recommendation, propensity, uplift, and lifetime-value pipelines; implement CI/CD, model versioning, monitoring, and orchestration; and lead agentic AI workflow automation. Collaborate with data science, engineering, marketing, and operations teams to productionize solutions. Provide technical leadership, mentor engineers and data scientists, and influence ML and data strategy.

Build and operate large-scale batch and streaming data pipelines and real-time feature infrastructure to support production ML. Develop ML-adjacent services, ensure reliability/observability, participate in on-call and incident response, and collaborate with ML, data, and platform teams to meet latency, availability, and data quality requirements.

Develop machine learning models and infrastructure for assessing autonomous vehicle behavior. Collaborate across teams to optimize production systems and model performance.

Design, build, and scale distributed machine learning platform services for Roku’s personalized recommendations. Develop feature stores, real-time inference systems, vector databases, APIs, and platform infrastructure serving millions of transactions per second. Run online A/B tests, monitor system and business metrics, optimize recommendation performance, collaborate with cross-functional engineering teams, and mentor team members on platform engineering practices.

Lead development of an AI-ready research data ecosystem for vaccine R&D: design integrated data architectures, automate ingestion and transformation pipelines, define semantic/metadata frameworks, build knowledge graphs and RAG systems, and develop multimodal predictive and translational ML models. Provide technical leadership, mentor teams, and collaborate across scientists, bioinformatics, and enterprise data organizations to accelerate AI-driven vaccine discovery and decision-making.

Lead technical direction and deliver large-scale ML platform infrastructure including feature store, model serving, experimentation, and training pipelines. Drive architecture, mentor engineers, coordinate cross-team initiatives, resolve system-wide issues (latency, reliability, GPU utilization), and define safe ML deployment paths from registration through rollout, monitoring, and rollback.

Lead end-to-end ML model development for safety-critical driving behaviors, design experiments, improve data quality, deploy models and metrics fleet-wide, and communicate results across product and engineering teams.

As a Machine Learning Engineer at FanDuel, you'll develop scalable ML applications, enhance user experience through intelligent systems, and collaborate across teams to validate AI insights.

The Machine Learning Engineer will design and implement search systems, develop scalable serving systems for ML, and collaborate with teams to productionize AI insights, requiring engagement with big data and machine learning frameworks.