Top Machine Learning Engineer Jobs

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

The Senior Machine Learning Engineer will architect the ML infrastructure, productionize models, maintain data platforms, and ensure system health, collaborating closely with data scientists and data engineers.

The Staff Machine Learning Engineer will architect and optimize data infrastructure for multimodal AI models in oncology, focusing on data management and processing pipelines.

Design, build, deploy, and maintain production ML models and data pipelines at scale. Collaborate with product and data science teams, apply ML best practices, monitor model performance, and use cloud and CI/CD tooling to ensure resilient, governed, and explainable ML systems.

Design and implement machine learning algorithms for robotic manipulation, focusing on RL and IL. Collaborate across disciplines to ensure integration and successful deployment.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

The Senior Software Engineer will lead technical areas, manage AI/ML projects, develop model services, APIs, and collaborate on design and UX to enhance cloud-based AI solutions.

As a Senior Software Engineer, you will build AI platform features, provide technical leadership, drive projects, and maintain high-performance systems primarily using Python and Go.

The Senior Software Engineer will lead technical projects, build AI/ML features, improve APIs, and collaborate on design, ensuring efficient operational practices.

The Senior Software Engineer will lead projects focused on building an AI platform for managing models, collaborating on features, and maintaining high performance in a scalable environment.

The Senior Software Engineer will provide technical leadership, build and scale AI/ML systems, maintain APIs, and collaborate on design while independently shipping product features.

Design, implement, and scale AI/ML systems and data pipelines for healthcare applications. Own model inference, deployment, monitoring, and reliability; collaborate with cross-functional teams to deliver production ML solutions using cloud and distributed computing.

Design, implement, and operate production AI/ML systems for healthcare. Own inference and delivery of ML insights, scale and monitor models, build and optimize data pipelines, apply MLOps practices, and partner with cross-functional teams to integrate AI into products.

Lead development of scalable ML infrastructure, focusing on compute for simulation, data labeling, and data generation. Collaborate with ML engineers, set technical roadmaps, ensure system reliability, and research advanced computing techniques.

The Senior ML Infrastructure Engineer will design, implement, and optimize computing platforms for simulation workflows, ensuring high reliability and performance. Responsibilities include leading technical decisions, collaborating with ML engineers, and enhancing system efficiency.

The Lead Machine Learning Engineer designs and implements ML applications, builds data pipelines, collaborates with teams, and ensures performance and governance of ML systems.

The Principal Engineer, AI/ML Architecture will define the technical direction for Coupa's AI platform, focusing on model training, evaluation, safety standards, and mentoring a team of ML and data engineers.

Join the Inference team to implement features and improvements for model serving on a GPU platform, focusing on performance and reliability.

The role involves optimizing large-scale ML infrastructure for autonomous vehicles, conducting analyses, collaborating with teams, and improving system performance.

As a Lead Machine Learning Engineer, you'll design and implement machine learning applications, collaborate with teams, ensure model performance, and build cloud-based infrastructures.

The role involves supporting AI/ML infrastructures, managing Linux and Windows systems, deploying ML Ops tooling, and collaborating with cross-functional teams.

Design, implement, and deploy scalable ML training and evaluation platforms. Own end-to-end projects, make architectural decisions, drive prioritization, collaborate cross-functionally, and mentor engineers to accelerate autonomous driving model development.

Build and deploy scalable ML training and evaluation platforms, drive technical projects and architectural decisions, collaborate with partner teams, prioritize platform improvements, ensure reliability and performance, and mentor junior engineers.

Develop scalable ML platform infrastructure, support ML training and deployment, collaborate on software tools for scientists, and enhance workflows and automation.

The Lead Machine Learning Engineer will design, build, and deploy machine learning models and data pipelines, ensuring high performance and governance of ML applications while collaborating in an Agile environment.