Top Machine Learning Engineer Jobs

The Machine Learning Research Engineer will design and develop robot learning algorithms for manipulation tasks, leveraging large-scale data collection to improve robotic behavior and collaborating with the robotics team.

As a Machine Learning Engineer in the Notifications team, you'll design and implement ranking and recommendation systems using LLMs, develop experiments, and collaborate with research teams to improve notification relevance and user experience.

Manage a team of Machine Learning Engineers, architecting systems for content integrity, and ensuring effective model training and deployment.

Lead and mentor a team of machine learning engineers, drive ML projects, and collaborate across departments to enhance product features and model improvements.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

Own and lead the entire technical stack from hardware and sensors to backend pipelines and ML inferencing. Coordinate software and hardware engineers, set technical direction, build data acquisition and processing pipelines, launch products, mentor junior staff, and ensure reliable, high-performance systems.

You will design and develop machine learning systems for an AI-driven dating platform, focusing on personalized matching algorithms and natural language interactions.

Develop technologies for optimizing AI models in autonomous driving. Collaborate on parallel algorithms and influence compute platform architecture.

The role involves leading machine learning initiatives to enhance automated testing for autonomous vehicles, improve model interpretability, and collaborate with cross-functional teams.

Lead the technical development of machine learning algorithms for the ML Planner, collaborating across teams to influence autonomous technology direction.

The Principal Machine Learning Engineer will develop multimodal technologies, mentor engineers, and drive product innovation in agentic AI systems.

Lead high-impact applied ML and analytics initiatives, manage data science workstreams, design ML pipelines, and collaborate with stakeholders to drive measurable outcomes.