Top Machine Learning Jobs in NYC, NY

As an MLOps Engineer II, you'll enhance ML workflows, support engineers, improve model deployment processes, and advocate for best practices across teams.

The AI Engineer Manager leads the Data and Analytics Engineering team in developing AI solutions, manages client accounts, analyzes data, and fosters team leadership and relationships with clients.

Design, build, and productionize ML models and infrastructure at scale. Collaborate with product and data science teams, develop and validate models, construct data pipelines, deploy and monitor models, apply CI/CD, and ensure Responsible/Explainable AI and high availability for ML systems.

As a Principal AI/ML Engineer, you will lead the development of machine learning solutions for advertising, focusing on optimization, personalization, and deploying models within a highly scalable infrastructure.

The role involves designing and developing machine learning applications, collaborating with Agile teams, optimizing ML systems, and ensuring high availability and performance of models.

Design and implement generative ML systems (image, video, audio, multimodal LLMs) and deliver on-device and server-side inference. Build GenAI pipelines and AR experiences, prototype with cross-functional teams, and optimize efficient models for real-time mobile and wearable applications.

The role involves designing and implementing AI and machine learning solutions, transforming data into insights, and supporting client engagements. Responsibilities include model development, data analysis, and enhancing model performance using established technologies.

Build and deploy transformer and sequential deep learning models using large-scale financial and behavioral data to power personalized growth and marketing experiences. Work cross-functionally to design scalable training, serving, and monitoring infrastructure (batch and real-time), contribute to experimentation and optimization frameworks, and maintain production-grade MLOps systems to improve member engagement and business metrics.

Lead the design, development, and implementation of Generative AI applications, ensuring high performance and scalability in cloud-native environments. Collaborate with AI/ML teams to drive innovation and apply best practices in machine learning engineering.

The Lead Machine Learning Engineer will design, build, and productionize ML applications, oversee model performance, collaborate with teams, and ensure reliable deployment of ML systems at scale.

Design, build, deploy, and maintain production machine learning models and pipelines at scale. Collaborate in cross-functional Agile teams to architect ML systems, automate testing and CI/CD, monitor model performance, and apply responsible and explainable AI practices. Leverage cloud platforms and distributed computing to optimize model training and serving.

Lead development and maintenance of AML typologies and analytics to detect and mitigate financial crime. Collaborate with cross-functional teams and FIU, build and deploy machine learning models, perform data transformation/ETL, and produce actionable insights. Provide technical leadership across analytics projects, mentor colleagues, and ensure solutions align with regulatory and enterprise risk frameworks.

The VP, AI Innovation & Enablement Leader will drive AI technology adoption, develop reusable patterns for engineering teams, and enhance enterprise AI capabilities.

This role entails using statistics and machine learning to analyze complex datasets, implementing algorithms, and back-testing models. A PhD is required for this position.

Design and build ML infrastructure, influence data access and model deployment, partner with researchers to optimize ML models for trading, and mentor engineers.

The Senior Machine Learning Engineer at DraftKings will design scalable ML pipelines, optimize models, implement monitoring systems, and lead technical initiatives in AI development.

The ML Data Engineer develops and maintains machine learning pipelines and systems, collaborating with teams to optimize manufacturing processes through data-driven insights and predictive models.

The AI Solutions Engineer will develop and deploy AI applications, working closely with customers to translate business goals into reliable services, while being involved in project management and product specifications.

The Director of Data Sciences leads a team in strategic initiatives, drives machine learning and AI solutions, and manages resources to enhance data-driven decision making across the business unit.

The Research Scientist will design evaluation measures for AI systems, collaborate with agencies, and publish research on AI risks.

As an Engagement Management Lead, you'll manage strategic customer relationships, oversee project delivery, and ensure quality outcomes while navigating complex customer dynamics.

This role involves developing AI applications, focusing on LLMs and agent frameworks, and requires research skills in ML with experience in data strategy.

Lead enterprise AI strategy and transformation: define AI and GenAI strategy, build AI Centers of Excellence, governance, and MLOps, prioritize high-value use cases, oversee AI product and platform deployment across cloud, lead cross-functional teams, advise executives, support business development, and ensure Responsible AI and compliance to deliver measurable business value.

The Engineering Manager, Data Science will lead a team to drive machine learning initiatives in AdTech, focusing on optimization and modeling strategies to enhance client success and operational efficiency.

Lead data analysis for drug discovery in neuroscience, integrating diverse datasets and collaborating with cross-functional teams to develop therapeutic candidates and validate targets.