Top Senior Machine Learning Jobs in NYC, NY

Develop applied AI, machine learning, optimization, and statistical models to solve complex business problems for Chase Finance and Business Management. Analyze large datasets, identify actionable insights, contribute throughout the modeling lifecycle, and communicate findings to senior stakeholders. The role also involves serving as a subject matter expert, advising partners, and introducing innovative analytical approaches.

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.

Build and validate ML and statistical models, perform exploratory and descriptive analyses, create visualizations, maintain data pipelines and quality, collaborate with clients to deliver tailored AI/ML solutions, mentor junior staff, and apply modern AI techniques including generative AI and NLP to solve business problems.

Lead product vision and execution for a portfolio of ML-powered personalization and insights products. Translate research and analytics into roadmaps, run experiments, partner with data science and engineering, ensure privacy/fairness, enable self-serve platforms, and measure outcomes across marketing, servicing, assistants, and financial management.

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.

Review and audit machine learning models and model risk practices across the enterprise. Partner with cross-functional teams to design, train, evaluate, validate, and implement ML and NLP solutions, using tools like Python, Spark, H2O, and AWS. Translate technical findings into business recommendations and assurance for stakeholders and the Audit Committee, while researching and applying emerging Generative AI and state-of-the-art methods.

Build and validate ML, optimization, and statistical models on large structured and unstructured consumer datasets; generate insights and actionable recommendations; communicate results to senior stakeholders; contribute across the full modeling lifecycle and advance team knowledge.

Lead development and deployment of optimization and machine learning models to improve retail banking decisions. Translate business problems into mathematical formulations, build production-grade optimization and simulation models, collaborate with Product and Engineering to integrate solutions, and run end-to-end model development, evaluation, validation, and implementation.

Contribute to machine learning model design, training, and evaluation for fraud detection. Communicate findings, develop metrics, and support production ML workflows while ensuring compliance and model governance.

Owner of product features for Card Intelligence ML initiatives; collaborate with data scientists, engineers, and stakeholders to define roadmaps, break down epics into MVPs, prioritize features, and accelerate model-to-market delivery for fraud prevention.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Build and maintain scalable machine learning infrastructure, including distributed GPU training pipelines, model promotion workflows, monitoring integrations, and ML frameworks. Optimize training throughput, support model deployment and observability, and collaborate with product, architecture, data science, and engineering teams. The role requires strong Python, cloud, ML framework, big data, training and inference system, and enterprise design experience, with preferred expertise in recommendation systems, CUDA, containers, orchestration, and data storage.

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.

Lead research and development of emerging ML (embeddings, foundation models, transformers). Build and productionize machine learning models at scale, partner with product and engineering, analyze large-scale transactional and clickstream data, and run experiments to improve customer experiences across marketing, servicing, and fraud domains.

Lead and grow a team of ML engineers building applications for a graphically intensive simulation platform. Manage stakeholders, translate customer needs into technical requirements, prioritize between off-the-shelf, open-source, and greenfield solutions, coordinate cross-functional delivery, and oversee the full ML lifecycle from data infrastructure to deployment. Foster team ways of working and performance.

Develop generative machine learning technologies and products serving millions of users. Build state-of-the-art image, video, language, and audio generation pipelines; deploy generative ML experiences on devices; create augmented reality experiences using generative and diffusion models; and collaborate with cross-functional teams to prototype new products.

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.

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.

As a Lead Machine Learning Engineer, you'll manage AI/ML projects, optimize models, enhance ad-serving platforms, and mentor junior engineers.

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.

Lead development of large-scale, low-latency ML systems for ad serving to optimize pricing, allocation, and auctions. Design experiments, build production ML pipelines and model serving, partner cross-functionally, and provide technical leadership and mentorship.

Design, build, and deploy large-scale machine learning models powering Snapchat features. Own full ML lifecycle from data analysis to production, prototype with cross-functional partners, apply modern ML techniques, and use AI tools while ensuring code correctness, security, and scalable system design.

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.