Top AI & Machine Learning Jobs

Build end-to-end data engineering and data science solutions for robotics and manufacturing data. Responsibilities include designing scalable multimodal data pipelines, creating analytic datasets, applying statistical and machine learning methods, developing MLOps workflows, building APIs and internal tools, and improving data governance, reliability, automation, and production support. The role partners cross-functionally with robotics, manufacturing, software, and data teams.

Lead and grow a software and AI engineering team to deliver AI-enabled enterprise applications on Glean and DBX Genie. Own end-to-end delivery, architecture, platform integration, CI/CD, security, and operational excellence while mentoring engineers and partnering cross-functionally to scale governed AI solutions.

Lead development and operation of CI platform components (RBE, FUSE) to enable scalable build, test, and developer workflows. Collaborate across teams, own projects end-to-end, drive engineering best practices, mentor engineers, perform design/code reviews, and support integration of internal tools with the FUSE-based file system to improve developer productivity for AV and ML workloads.

Lead development and deployment of AI/ML solutions using telematics and large-scale data. Build and maintain data pipelines, prototype generative and agentic AI, train and validate models, guide data scientists, perform statistical analysis and reporting, and implement MLOps in cloud environments to surface vehicle safety insights.

Build and scale the ML compute backend: develop core backend services, integrate with GPU hardware and orchestration systems, optimize GPU utilization and system performance, ensure reliability and scalability, work with cloud providers and Kubernetes at scale, and lead large cross-team initiatives to improve ML platform usability and efficiency.

The Senior AI/ML Capacity Engineer will develop capacity planning models, design analytical tools, and collaborate with teams to optimize costs and efficiencies.

As an ML Infrastructure Engineer, design backend components, enhance platform performance, and drive system improvements while collaborating across teams.

Design, build, and deploy scalable ML training and evaluation platforms for autonomous driving. Lead architecture and implementation of distributed, high-performance pipelines, drive cross-team prioritization, mentor engineers, and support recruiting and code quality to accelerate ML model development lifecycle.

Develop and implement validation methods, tools, and production code to analyze autonomous vehicle behavior using simulation and test data. Build scalable testing products, generate data-driven insights for AI and systems engineers, and establish validation best practices across teams.

Lead development and productionization of advanced forecasting and predictive models: assess existing analytics, define data needs, design methodologies, run proofs-of-concept, architect cloud-based model deployments, and monitor model performance while translating results for business stakeholders.