Top Machine Learning Engineer Jobs in NYC, NY

Partner with quantitative researchers to design, build, optimize, and scale deep learning models and tooling for research and production. Work includes model architecture, distributed training, inference optimization, ML libraries, and high-performance computing to improve training speed, scalability, reliability, and cost-efficiency in systematic investing workflows.

Build, deploy, scale, and maintain machine learning models and infrastructure for production use. Responsibilities include designing ML solutions, optimizing models and data pipelines, operating distributed systems and cloud platforms, automating testing and deployment, monitoring production models, and applying responsible AI and software development practices. The role collaborates with Product, Data Science, and Agile engineering teams.

Leads the design, development, and scaling of machine learning systems for signals, attribution, recommendation, ranking, and advertising measurement. Provides technical direction, improves ML infrastructure, collaborates with cross-functional leadership, and promotes scalable, reliable, cost-effective engineering practices. The role requires shipping advanced models, mentoring engineers, solving ambiguous technical problems, and influencing Snap’s broader machine learning strategy.

Lead the design, development, deployment, and operation of machine learning models and platforms at scale. Build distributed ML infrastructure, data pipelines, cloud-based architectures, and production services. Develop and test application code, automate deployment, monitor and retrain models, and apply CI/CD, governance, security, and responsible AI practices. Collaborate with Product, Data Science, and Agile teams to solve complex business problems.

Lead MLOps engineering for recommendation systems: build distributed GPU training pipelines, real-time and batch serving, deploy quantized LLMs, manage vector databases, implement monitoring/observability, optimize performance and reliability, and collaborate with product and architecture teams to scale AI infrastructure in AWS.

Design, build, deploy, retrain, monitor, and maintain production machine learning models and infrastructure. Develop application code, data pipelines, testing, automation, and cloud-based architectures using distributed systems and containerized environments. Collaborate with Product and Data Science teams in Agile delivery, apply responsible and explainable AI practices, optimize model performance, and support resilient deployments at scale.

Build, deploy, scale, and operate production machine learning models and platforms. Responsibilities include developing ML components, distributed data pipelines, cloud and Kubernetes infrastructure, model monitoring and retraining, CI/CD automation, responsible AI governance, and collaboration with product, data science, and engineering teams.

Design, build, deploy, optimize, and monitor production machine learning models and infrastructure. Develop scalable data pipelines, distributed systems, cloud-based architectures, and automated testing and deployment processes. Collaborate with Product and Data Science teams, apply responsible AI practices, maintain model governance, and support resilient production services using modern ML frameworks, Kubernetes, and cloud platforms.

Build, deploy, scale, and operate production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, managing cloud-based distributed systems, automating testing and deployment, monitoring production models, and applying responsible AI practices. The role collaborates with Product and Data Science teams in an Agile environment and uses technologies such as Python, PyTorch, Spark, Kubernetes, and cloud platforms.

Design, build, deploy, monitor, and maintain production machine learning models and infrastructure. Develop optimized data pipelines, distributed systems, cloud-based architectures, and automated testing and deployment processes. Apply modeling, feature selection, validation, responsible AI, and explainability practices while collaborating with product, data science, and engineering teams in an Agile environment.

Build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML applications, distributed data pipelines, cloud and Kubernetes infrastructure, automated testing and deployment, model monitoring, retraining, governance, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while solving complex business problems using Python, Scala, Java, and large-scale ML technologies.

Design, build, deploy, and operate machine learning models, platforms, data pipelines, and production services at scale. Responsibilities include model development, infrastructure optimization, cloud and Kubernetes operations, automated testing, CI/CD, monitoring, retraining, governance, responsible AI, and collaboration with product, data science, and Agile engineering teams.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include designing ML systems, developing data pipelines, operating distributed systems and cloud infrastructure, automating testing and deployment, monitoring and retraining models, and applying responsible AI practices. The role collaborates with Product and Data Science teams and uses Python, Java, Scala, or related languages.

Build, deploy, scale, and maintain machine learning models and platforms in production. Develop optimized data pipelines, distributed ML systems, cloud-based architectures, and automated CI/CD workflows. Collaborate with product and data science teams, monitor and retrain models, apply responsible AI practices, and ensure secure, governed software delivery. The role requires extensive experience with ML frameworks, distributed systems, cloud services, Kubernetes, and production machine learning operations.

Build, deploy, scale, and maintain production machine learning models and infrastructure. Develop optimized data pipelines, cloud-based architectures, and distributed ML systems using frameworks such as PyTorch and TensorFlow. Apply model validation, tuning, monitoring, responsible AI, testing, CI/CD, and deployment best practices while collaborating with Product, Data Science, and Agile engineering teams.

Build, deploy, scale, and operate machine learning models and platforms in production. Responsibilities include designing ML systems, developing data pipelines, optimizing models and infrastructure, managing cloud-based containerized services, automating testing and deployment, monitoring and retraining models, and applying responsible AI practices. The role partners with Product and Data Science teams and uses Python, Scala, or Java alongside distributed systems and cloud technologies.

Build, deploy, optimize, and maintain production machine learning models and infrastructure. Responsibilities include developing ML applications, designing data pipelines, operating distributed systems and cloud platforms, monitoring and retraining models, automating testing and deployment, and applying responsible AI practices. The role collaborates with Product and Data Science teams to deliver scalable solutions using frameworks such as PyTorch or TensorFlow and platforms including AWS, Kubernetes, Spark, and Ray.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include developing ML algorithms, optimizing cloud-based infrastructure, constructing data pipelines, operating distributed systems, automating testing and deployment, monitoring models, and applying responsible and explainable AI practices. The role requires collaboration with product, data science, and cross-functional Agile teams.

Design, build, deploy, retrain, and monitor production machine learning models and components. Develop scalable ML infrastructure, data pipelines, cloud architectures, and distributed systems using Python and related technologies. Collaborate with Product and Data Science teams, automate testing and deployment, apply CI/CD practices, and ensure model governance, security, responsible AI, and explainability across enterprise platforms.

Own and evolve Braze’s ML platform for training, deploying, serving, and monitoring models at global scale. Lead complex infrastructure initiatives, multi-region model serving, CI/CD, orchestration, reliability, cost optimization, and incident response. Set technical direction, improve production quality, collaborate across teams, mentor senior engineers and data scientists, and connect platform decisions to business outcomes.

Lead the end-to-end development and operation of machine learning systems supporting customer retention, ranking, matching, recommendations, pricing, and lifetime value growth. Build scalable data pipelines and ML infrastructure across batch and real-time environments, while implementing model monitoring, observability, deployment, and performance optimization. Collaborate across engineering and science teams and promote strong software engineering practices.

Design, build, and deploy scalable AI and ML solutions by wrangling and integrating data, implementing models (Python/Java, TensorFlow, Scikit-Learn), developing data pipelines and NLP applications, collaborating with clients, and mentoring junior team members to drive business insights and regulatory risk solutions.

Design, build, and deploy AI and machine learning solutions at scale. Develop data pipelines and scalable models, perform data wrangling and analysis, apply NLP and neural networks, collaborate with clients and teams, and contribute to open-source and iterative improvement of AI systems.

Own the architecture and production delivery of a novel AI-powered exploitability engine. Lead probabilistic ranking, graph modeling, entity resolution, calibration, validation, and security guardrails for agentic systems. Drive zero-to-one technical direction, architecture, code reviews, cross-functional partnerships, and mentorship. Build scalable systems using distributed technologies, cloud-native development, APIs, databases, and machine learning while establishing defensible engineering decisions and evaluation metrics.

Develop and test machine learning models, pipelines, agentic systems, and LLM-powered applications under senior-engineer mentorship. Interns will work across the ML lifecycle, including problem framing, model selection, pipeline development, evaluation, and deployment support. The role involves collaborating with ML Engineers, Product Managers, Designers, and Full-Stack Engineers on scalable AI solutions.