Top Machine Learning Jobs in NYC, NY

As a Data Science Lead, you'll drive AI/ML initiatives, mentor data scientists, collaborate with stakeholders, and ensure effective model deployment and governance.

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.

Lead data-science work for matching features: translate product goals into data problems, design and analyze A/B tests, perform exploratory analysis and data mining, generate strategic insights, and collaborate with AI, product, design, and engineering to develop and iterate production ML models.

As a Senior Lead AI Solutions Consultant, you will drive revenue through the adoption of BrazeAI tools, managing technical discussions, running proofs-of-concept, and educating clients on AI concepts.

Use advanced analytics, statistical methods, and machine learning to transform large datasets into actionable insights. Build data solutions and pipelines, perform exploratory analysis, create visualizations, validate models (including generative AI and NLP), collaborate with clients, mentor juniors, and maintain data quality to inform strategic decisions.

Lead data science initiatives within Data and Analytics Engineering to build predictive models, maintain data pipelines and lakes, apply ML/DL and generative AI, ensure data quality, coach teams, manage project timelines, and collaborate with stakeholders to drive data-driven business growth.

Lead product development for Celonis/Ikigai decision intelligence within the Celonis Context Model. Partner with ML researchers and engineers to define scope, prioritize backlog, deliver real-time decisioning and predictive features, optimize model lifecycle (onboarding, evaluation, feedback), and coordinate cross-functional integration into the core platform and customer solutions.

Summer internship conducting quantitative research to develop, backtest, and implement predictive trading models using advanced statistical and machine learning techniques, unconventional data sources, and coding in Python/R/C++. Collaborate with senior researchers and participate in live trading deployment and analysis.

The role involves model validation, monitoring AI model performance, challenging model developers, and translating technical requirements into practical evaluations and controls for AI and ML models.

Lead data science projects to advance drug development, implementing models for patient selection and clinical trial optimization, and collaborating with cross-functional teams.

Lead data science projects to optimize drug development, develop AI models for clinical trials, and collaborate with teams on biomedical workflows.

11-week quantitative research internship developing and implementing mathematical and trading models. Responsibilities include model conceptualization, backtesting, live implementation, using unconventional data, and conducting statistical research to refine trading signals and monetization systems.

Lead strategy and execution for enterprise intelligence by framing complex AI, data, and technology questions; partner with senior leaders to design and deploy intelligence products; create executive- and Board-level communications; run cross-functional initiatives and governance; and support enterprise planning, portfolio management, and adoption of AI-enabled capabilities.

Lead Gusto's AI/ML strategy, managing teams in Machine Learning Engineering, AI, and Risk Data Science. Ensure systems are reliable and impactful while fostering a culture of technical excellence.

The AI Engineer will focus on building AI experiences, deploying models, and enhancing data workflows, working on greenfield applications. Key responsibilities include feature development, architecture collaboration, and shipping impactful product experiences.

Lead evaluation strategy for Datadog Dashboards' AI-native system: define metrics, build eval datasets and regression harnesses, measure retrieval and tool-selection, reduce cost/latency, and provide technical leadership and cross-team mentorship to ensure production-quality agent behavior at scale.

The AI Researcher will conduct advanced research in Generative AI and Machine Learning, develop specialized models for observability and code repair, and collaborate with product teams to integrate findings into Datadog's offerings.

As an AI Product Specialist, you will define strategies for market expansion and revenue growth of AI-powered solutions, integrating technical understanding with market leadership.

Develop, validate, and implement machine learning models using big-data tools and cloud platforms. Partner with engineers and product managers to translate analytics into production products, analyze large numeric and textual datasets, and communicate findings to drive business decisions for commercial banking portfolio forecasting.

Implement and optimize C++ core algorithms for machine learning, computer vision, and 3D/procedural modeling. Apply cutting-edge research, manage code with Git, deploy and test on cloud and Unix-based systems, and work with large-scale geospatial datasets to generate user-facing 3D content.

Lead the development of fraud prediction models using machine learning, collaborating across teams to build, scale, and monitor models in production.

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

As a Senior AI Engineer, you'll design and implement AI-driven solutions, focusing on full-stack development and production-grade applications while mentoring junior team members.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

Provide legal guidance on Rubrik's AI use and products, ensuring compliance with laws and contracts, drafting and negotiating AI terms, coordinating generative AI review processes, educating employees, and improving AI review controls and workflows across cross-functional teams.