Top Machine Learning Engineer Jobs in NYC, NY

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 will build data pipelines, manage multi-GPU training infrastructure, optimize code, and deploy inference pipelines for LLMs.

As a Machine Learning Engineer at Verneek, you'll develop AI/NLP models, tackle complex AI research problems, and innovate within an early-stage deep-tech startup environment.

Own and architect Garner's production ML platform, ensure reliability, performance, cost-efficiency, implement ML CI/CD, drift monitoring, observability, and set standards for future MLOps team.

Build, operate, and improve production ML systems and platform components (feature store, model registry, CI/CD). Ensure reliability, observability, SLOs, on-call support, drift monitoring, and cost/latency optimization. Partner with ML, data, and product teams to automate model deployment, implement data quality checks and statistical validation, and define MLOps standards and KPIs.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

Build and maintain an ML feature platform: backend systems for feature creation, exploration, serving, data storage and availability, and offline backfilling. Deliver reliable, scalable, well-tested solutions that enable MLEs, analysts, and decisioning teams; debug large codebases and collaborate across engineering to meet availability and latency goals.

As an ML Engineer, develop and deploy advanced ML solutions for OCR, document processing, and voice technologies, collaborating with teams to optimize models for performance.

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.

Design, build, and deploy AI systems, manage the ML lifecycle, collaborate with teams, and advocate for new techniques in machine learning.

The role involves designing and improving AI/ML platforms and operational infrastructure, collaborating with engineers and product managers, and mentoring teams on MLOps best practices.

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.

The role involves developing an ML research platform, managing data pipelines, and building tools for simulation lifecycle and performance monitoring in a collaborative environment.

The role involves engineering and deploying AI agents, collaborating with teams on solutions, and managing data flows. The position requires deep expertise in ML technologies and frameworks.

Lead ML engineer responsible for designing, building, deploying, and monitoring production ML systems at scale. Collaborate with product and data science teams, develop ML architectures and data pipelines, apply CI/CD and cloud-based MLOps, retrain and govern models, and ensure performance, reliability, and responsible/explainable AI practices.

Lead design, build, and productionize scalable ML models and pipelines. Collaborate with product and data science teams, implement cloud-based architectures, ensure CI/CD, monitoring, model governance, and responsible AI practices.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

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

As a Senior ML/AI Engineer, you will design and deploy a conversational interface, focusing on ML applications, latency optimization, and user experiences.

The Staff ML Engineer will define and build ML capabilities, focusing on production systems, strategy, and infrastructure in a cybersecurity context.

As an AI/ML Engineer, you'll design, build, and ship large scale NLP and LLM systems for classification, ranking, and content generation while collaborating with cross-functional teams.

As an Applied Machine Learning Engineer, develop, deploy, and optimize machine learning models while collaborating with cross-functional teams to enhance customer solutions.

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.

Responsible for fine-tuning state-of-the-art LLMs, optimizing models for deployment, and driving AI architectural decisions. Collaborate with cross-functional teams to develop impactful AI products.

Design, build, and maintain scalable AI/ML pipelines for data processing, model training, and deployment. Collaborate with data scientists and engineers to implement and optimize AI solutions, troubleshoot pipeline issues, document best practices, and develop MVP code to transition into production-ready systems.