Top Senior Machine Learning Jobs in NYC, NY

As a Senior AI Engineer, you'll develop and deploy innovative AI solutions for the fashion industry, collaborate with cross-functional teams, and refine machine learning models to enhance product offerings.

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.

As a Principal Applied Scientist, you'll define the technical vision for offer optimization and conversion modeling systems, ensuring alignment across teams while overseeing large technical initiatives and contributing to real-world machine learning solutions.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

The Machine Learning Engineer develops production ML algorithms and recommendation systems, collaborates with teams, and optimizes user experiences.

Develop and improve machine learning models and systems for clinical outcomes. Lead ML initiatives, mentor teams, and establish best practices in AI. Collaborate across engineering, product, and design for data-driven solutions.

Lead the development of production-grade ML systems, aligning technical roadmaps with company priorities, mentoring ML engineers, and ensuring ethical use of ML/AI.

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.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

As Lead ML Engineer, you will architect and build a conversational AI interface, optimize models for deployment, and collaborate with a multidisciplinary team.

The role involves designing and implementing large-scale ML solutions for personalized music experiences, collaborating with cross-functional teams to enhance user interactions and fulfill music recommendations.

Lead design, development, deployment, and monitoring of statistical and machine learning models for trading and risk use cases. Build low-latency Java algorithmic trading solutions, support real-time production, perform data acquisition and cleaning, collaborate with product, sales, and quant researchers, and mentor teams to uplift ML capability.

Design, build, and productionize large-scale foundation models and ML systems (transformer architectures, self-supervised learning). Architect and implement training, data pipelines, deployment, monitoring, and CI/CD. Collaborate with cross-functional teams, ensure model governance, reliability, and performance, and apply ML engineering best practices at scale.

The Senior Machine Learning Engineer leads technical direction for ML systems focused on personalization and ranking, overseeing architecture and implementation while mentoring team members.

The Machine Learning Engineer will design, build, and deploy ML applications at scale, collaborating with cross-functional teams and applying best practices in ML engineering and cloud technologies.

Lead a team of Machine Learning Engineers in designing, building, and implementing ML applications, focusing on data-intensive solutions and infrastructure decisions, while promoting best practices in AI.

As a Machine Learning Engineer in Fraud Risk, you'll build and maintain ML systems for fraud detection and anomaly analysis, ensuring robust performance and real-time decision making.

Lead design, development, and deployment of end-to-end recommendation systems. Build, tune, and evaluate scalable recommendation algorithms, integrate ML into products, lead projects, mentor teams, and communicate solutions to clients and stakeholders while driving innovation in replenishment and supply chain analytics.

Build and maintain production AI/ML models and pipelines for an NDR platform; develop scalable, secure backend services; optimize detection algorithms and reduce false positives; integrate ML features into product architecture; mentor engineers; participate in code reviews, technical strategy, and documentation; operate cloud-native, containerized services and manage infrastructure as code.

Lead design and deployment of reinforcement learning and sequential decision models for collections and recovery. Build scalable ML pipelines (Databricks/Spark), run experimentation and offline policy evaluation, collaborate with engineering/MLOps to productionize models, and mentor junior data scientists.

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.

Lead and mentor ML engineers and data scientists, overseeing ML strategy and development of scalable models for healthcare applications.

Lead a multidisciplinary team to design, deploy, and optimize ML-driven bidding systems and measurement for a nine-figure marketing budget. Drive experimentation, partner strategy, media-mix optimization, and end-to-end measurement while aligning Marketing, Analytics, Engineering, and Product to improve marketing ROI and scale performance.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.