Top Machine Learning Engineer Jobs in NYC, NY

Designs, scales, and leads machine learning systems that detect harmful content and bad actors, support content moderation, and promote platform integrity. Provides technical direction across the ML organization, partners with cross-functional leadership, improves ML infrastructure and operational excellence, and delivers performant, scalable models using modern deep learning techniques.

Build, deploy, scale, and monitor production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing algorithms and data pipelines, operating distributed systems and cloud services, automating testing and deployment, maintaining models, and applying responsible AI, governance, security, and explainability practices. The role collaborates with Product, Data Science, and Agile engineering teams.

Lead the architecture and development of large-scale machine learning systems, distributed training infrastructure, ranking systems, and production ML services. Drive complex initiatives from concept through deployment, establish engineering standards, partner with product and engineering leadership, and mentor data scientists and engineers. Build reliable, low-latency, high-availability AI platforms using advanced ML frameworks, accelerators, and big data technologies.

Design, build, optimize, and scale machine learning models and systems for quantitative investment research and production. Collaborate with quantitative researchers and engineers on model architecture, distributed training, inference optimization, research tooling, and internal ML libraries. Improve training speed, scalability, reliability, performance, and cost efficiency across high-performance computing environments while translating research needs into robust technical solutions.

Ten-week machine learning engineering internship focused on building systems for training and deploying large-scale ML models. Interns collaborate with researchers, hardware experts, and software engineers on GPU acceleration, distributed computing, and open-source tools supporting trading strategies. The program includes technical training, mentorship, professional development, financial markets education, and team activities. Successful interns may receive an opportunity for a graduate position.

Build and operate production trust and safety systems that detect and disrupt platform abuse at internet scale. Responsibilities include developing ML infrastructure, training and feature pipelines, serving systems, evaluation frameworks, and LLM/classical ML detection solutions. Own systems from model integration through deployment, monitoring, and iteration while collaborating with security, product, and infrastructure teams.

Build, deploy, scale, and monitor production machine learning models and infrastructure. Develop optimized data pipelines, cloud-based architectures, automated testing, and CI/CD workflows. Collaborate with product, data science, and Agile teams while applying responsible AI, model governance, and software engineering best practices. The role requires expertise in machine learning frameworks, distributed systems, cloud operations, Kubernetes, and production model deployment.

Build and lead production machine learning capabilities for Snapchat+ subscription growth and monetization. Responsibilities include developing personalization, ranking, propensity modeling, offer decisioning, retention, and lifecycle optimization systems; translating ambiguous business problems into ML solutions; conducting experimentation; deploying scalable services; and establishing technical direction. The role partners with Product, Data Science, Backend, and Mobile Engineering teams while measuring impact on revenue, subscriber value, and retention.

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.

Design, train, fine-tune, optimize, and deploy large-scale machine learning systems for cloud security use cases. Build end-to-end ML pipelines, develop transformer and embedding models, productionize open-weight language models, and optimize inference for latency, cost, and quality. Architect resilient ML services across AWS and GCP using cloud-native microservices while collaborating with engineering teams on AI strategy and solving complex problems involving massive datasets.

Build, deploy, and maintain machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Use AI tools to develop scalable services while ensuring code correctness, security, performance, and architectural quality. Mentor collaborators and solve ambiguous, large-scale problems.

Build and deploy machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Develop scalable, secure, production-ready services using AI-assisted engineering workflows, while contributing to ranking, recommendations, search, content understanding, or image-generation applications.

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.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

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.

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.

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.

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 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.

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.

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.

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.