Top Machine Learning Jobs in NYC, NY

Design and implement AI systems and scalable machine learning models, perform data wrangling and software engineering to deploy AI solutions, interpret data for client recommendations, collaborate across teams, uphold technical standards, and build client relationships while navigating complex, ambiguous problems.

Lead development and deployment of AI/ML and generative-AI solutions (LLMs, RAG) that drive commercial growth and operational efficiency. Partner with product, engineering, and business teams to design, evaluate, monitor, and scale production systems using cloud, data engineering, and DAG orchestration frameworks while championing responsible AI and model governance.

Perform quantitative analysis, data mining, and statistical modeling to generate actionable product and business insights. Design and track core metrics, build dashboards and visuals, collaborate with cross-functional teams, use AI tools to scale analyses, and drive projects to completion to inform product decisions.

Lead product-focused quantitative analyses and machine learning efforts to generate actionable insights, drive product decisions, and optimize features. Collaborate with cross-functional teams to design metrics, build scalable statistical solutions, create dashboards, and communicate findings while using AI tools responsibly.

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.

Summer Associate will develop quantitative models and algorithms to support algorithmic trading, pricing, hedging, and portfolio optimization; collaborate with traders, technologists, and risk managers; conduct alpha research and deploy data-driven trading strategies.

Summer analyst will build and maintain quantitative models and algorithms for systematic trading and market-making, value and hedge transactions, conduct alpha research, calibrate models, optimize portfolios, and collaborate with traders, technologists, risk, and sales to deploy data-driven trading strategies.

Lead strategy and execution of ML-powered personalization and next-best-action across channels; define product vision, roadmaps, and KPIs; partner with data science and ML engineering to design, evaluate, deploy, and monitor LLM/ML solutions; own operating-memory/context data products, API-first cloud delivery, experimentation, and mentor product teams while ensuring privacy and fairness.

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 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.

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.

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.

Develop, train, and deploy machine learning and generative AI models in production. Focus on engineering aspects: scalable software, MLOps/DevOps integration, CI/CD pipelines, monitoring (e.g., New Relic), and automation frameworks to ensure model reliability and performance.

Work on statistical and machine learning solutions and generative AI (LLMs) projects. Translate business questions into models, perform exploratory analysis, feature engineering, model validation, and communicate results to technical and non-technical stakeholders throughout an 11-week summer internship.

Build and own fraud metrics, dashboards, and reporting. Design and implement real-time monitoring, alerts, and predictive ML-driven risk controls. Balance loss savings with customer friction, run experiments (A/B tests), partner with GTM teams, summarize findings to stakeholders, and assist teams with fraud triage.

As a Principal Associate Data Scientist, you'll build and manage machine learning models for marketing efficiency, translating business goals into data solutions and collaborating with stakeholders.

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.

Develop, validate, and maintain RMBS prepayment and credit/default models; perform large-scale data processing and ML analysis; build analytics platforms and monitoring tools; support valuation, hedging, risk assessment, model review, and client/internal training.

Lead a portfolio of full-stack, cloud-based projects delivering personalized omnichannel experiences. Design, develop, test, and implement AI/ML and LLM-driven solutions using modern cloud infrastructure and a polyglot tech stack. Mentor engineers, conduct code reviews, collaborate with product managers, and drive technical direction for scalable, high-performance systems.

Lead and grow a team of data scientists for SoFi’s consumer finance businesses. Prioritize and deliver high-impact data science and ML projects, build data pipelines, deploy and monitor models in production, mentor engineers, recruit talent, and collaborate cross-functionally with product, engineering, finance, risk, and executives to drive data-informed business decisions.

Lead design and delivery of AI/GenAI solutions and scalable data infrastructure. Drive business development, manage multiple client engagements, mentor teams, implement AI models using Python and C++, ensure data quality, and promote innovation across projects.

Design, train, evaluate, and productionize machine learning models (including embeddings and foundation models) on large-scale data. Partner with product and engineering to run experiments, improve customer experiences, and deploy scalable solutions using Spark and AWS.

Lead AI strategy and portfolio for Chase Wealth Management, identify and prioritize high-impact AI use cases, partner with product and technology to deliver solutions, influence senior leadership with data-driven narratives, develop governance and measurement frameworks, and drive cross-functional change to improve advisor productivity and client outcomes.