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

The Design Manager will lead a team creating intuitive in-vehicle system utilities, oversee design processes, and ensure usability across GM platforms.

This role involves leading a design team to create and execute strategies for digital instrument clusters, ensuring safety and usability in driver information design. Responsibilities include overseeing the design process, collaborating with cross-functional teams, and establishing design guidelines.

Lead multiple engineering teams to define technical strategy and deliver cloud-native, data-intensive Home Energy platforms (mobile, web, APIs, event streams, data services). Own architecture, reliability, security, delivery processes, metrics, and cross-functional partnerships while growing and mentoring engineering talent and driving platform innovation and integrations with utilities and external partners.

Design and maintain software for advanced driver assistance and autonomous vehicle simulations, focusing on scalable solutions and CI/CD.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

The Senior Cloud Engineer will manage Kubernetes clusters, implement PaaS features, optimize infrastructure, and enhance operational excellence while mentoring peers.

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.