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About the Team: The ML Inference Platform is part of the AV ML Infrastructure organization. Our team owns the cloud-agnostic, reliable, and cost-efficient platform that powers GM’s AI efforts. We’re proud to serve teams developing autonomous vehicles (L3/L4/L5), as well as other groups building AI-driven products for GM and its customers. We enable rapid innovation and feature development by optimizing for high-priority, ML-centric use cases. Our platform supports the serving of state-of-the-art (SOTA) machine learning models for experimental, online and bulk inference, with a focus on performance, availability, concurrency, and scalability. We’re committed to maximizing GPU utilization across platforms (B200, H100, A100, and more) while maintaining reliability and cost efficiency. About the Role: We are seeking a Senior ML Infrastructure engineer to help build and scale robust platforms for ML Inference workflows. In this role, you’ll work closely with ML engineers and researchers to ensure efficient model serving and inference in production, for workflows such as data mining, labeling, model distillation, evaluations, simulations and more. This is a high-impact opportunity to influence the future of AI infrastructure at GM. You will play a key role in shaping the architecture, roadmap and user-experience of a robust ML inference service supporting real-time, batch, and experimental inference needs. The ideal candidate brings experience in designing distributed systems for ML, strong problem-solving skills, and a product mindset focused on platform usability and reliability.
Job Responsibility:
Design and implement core platform backend software components
Collaborate with ML engineers and researchers to understand critical workflows, parse them to platform requirements, and deliver incremental value
Lead technical decision-making on model serving strategies, orchestration, caching, model versioning, and auto-scaling mechanisms for highly optimized use of accelerators
Drive the development of monitoring, observability, and metrics to ensure reliability, performance, and resource optimization of inference services
Proactively research and integrate state-of-the-art model serving frameworks, hardware accelerators, and distributed computing techniques
Lead technical initiatives across GM’s ML ecosystem
Raise the engineering bar through technical leadership, establishing best practices
Contribute to open source projects
represent GM in relevant communities
Requirements:
5+ years of industry experience, with focus on machine learning systems or high performance backend services
Expertise in either Python, C++ or other relevant coding languages
Expertise in ML inference, model serving frameworks (triton, rayserve, vLLM etc)
Strong communication skills and a proven ability to drive cross-functional initiatives
Ability to thrive in a dynamic, multi-tasking environment with ever-evolving priorities
Nice to have:
Deep expertise building zero-to-one ML infrastructure platforms
Experience working with or designing interfaces, apis and clients for ML workflows
Experience with Ray framework, and/or vLLM
Experience with distributed systems, and handling large-scale data processing
Familiarity with telemetry, and other feedback loops to inform product improvements
Familiarity with hardware acceleration (GPUs) and optimizations for inference workloads