Top AI & Machine Learning Jobs

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.

Lead design and development of full-stack .NET solutions and AI-powered agentic workflows. Build RAG/LLM systems, microservices on AKS, ETL with Azure Synapse, integrate enterprise APIs, maintain CI/CD, mentor engineers, and support DevOps/SRE practices.

Lead design, build, and operate scalable observability systems for distributed cloud and on-vehicle environments. Contribute code and reviews, create technical designs, build tooling to collect observability signals, run PoCs for new tools, partner with engineering teams, define SLIs/SLOs, guide roadmap decisions, and mentor engineers to improve developer productivity and reliability.

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.

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.