Top Machine Learning Jobs

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

As a Senior Machine Learning Engineer in the Health team, you'll design and productionize ML systems for personalized health metrics, collaborating with data scientists and engineers to optimize performance and ensure service reliability.

As a Machine Learning Engineer II, you'll design, implement, and test machine learning systems, collaborating with various stakeholders to enhance predictive applications.

The Machine Learning Intern will collaborate with senior engineers to design and implement ML algorithms, work on production-related challenges, and participate in Hackathons to enhance user experience across Tinder's platforms.

The Machine Learning Engineer will develop and optimize large-scale ML models, build low-latency inference pipelines, and collaborate with teams to enhance performance and automate ML processes.

As a Senior Machine Learning Engineer, you will design ML infrastructure, lead zero-to-one initiatives, collaborate on projects, and enhance scalability in the development of ML applications.

Develop and productionize a next-gen Dispatch System, build ML models, automate processes, and collaborate with various teams while mentoring other engineers.

This role involves building and optimizing ML systems and frameworks, collaborating with teams to enhance research and development in ML models.

Design and deploy production-grade ML systems, integrate models into software pipelines, and develop features leveraging ML for optimization and prediction problems.

Design and deploy machine learning algorithms, develop production systems, and collaborate with stakeholders to align ML solutions with business objectives.

The Software Engineer will collaborate with researchers to develop and deploy ML models for pricing, handling model operations and challenges.

The AI/ML Engineer will design and deliver scalable ML solutions, improve existing tools, write maintainable code, and collaborate with teams.

The Senior Software Engineer - Machine Learning at Celonis focuses on designing and developing AI/ML product features and customer solutions, ensuring high quality and scalability while collaborating with various teams.

Responsible for designing and developing AI/ML product features and infrastructure to empower intelligent business decisions and operational excellence for customers.

As a Machine Learning Engineer, you'll design, build, and deploy ML applications, collaborate with teams, and optimize models and data pipelines. You'll focus on both model development and ensuring high performance in production environments.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

As a Senior Machine Learning Engineer, you will design, build, and deploy large-scale ML systems to enhance Dropbox's AI features, collaborating with teams and mentoring peers.

As a Machine Learning Intern, you will work on advanced AI solutions, collaborate with teams to design ML systems, and analyze data to enhance user experiences at Dropbox.

The role involves enhancing developer tools for GM's AV engineers, addressing pain points, and mentoring team members to improve productivity and maintain quality code efficiency.

Lead a local AI/ML team, drive AI innovations, mentor team members, and manage projects within the ML development lifecycle using Agile methodology.

The role involves leading AI/ML initiatives, developing machine learning models, collaborating across teams, and operationalizing models in production.

As a Lead Machine Learning Engineer, you'll design and scale ML infrastructure, collaborate across teams, and mentor engineers in AI and ML innovations.

The Machine Learning Engineer role involves designing, implementing, and deploying ML solutions, focusing on LLMs and collaboration with engineering teams.

Lead machine learning projects, collaborate with business partners, and manage data processes from research to production to drive business impact.

Develop ML force fields for life and materials sciences, managing data sets, improving software accuracy, and communicating results with the team.