Data & ML Infrastructure Architect

Summary

Join Motional as a Machine Learning & Data Infrastructure Architect to lead the technical vision and architecture for the systems powering our machine learning lifecycle. This mission-critical role involves shaping the infrastructure supporting terabytes of daily sensor data and petabyte-scale datasets for autonomous vehicle development. You will own the architecture of Motional’s ML data infrastructure, design and evolve infrastructure for petabyte-scale machine learning workflows, and architect high-throughput systems for distributed training. Responsibilities also include establishing robust data governance and collaborating with cross-functional teams. You will lead technical strategy and roadmap development, mentor engineers, and promote engineering excellence. This role requires 15+ years of software engineering experience with significant architecture-level ownership in ML, data infrastructure, or high-scale systems.

Requirements

• 15+ years of meaningful software engineering experience, including significant architecture-level ownership in ML, data infrastructure, or high-scale systems
• Proven experience leading the design of ML platforms that serve large-scale training and inference workloads
• Deep technical fluency in distributed storage, high-volume data pipelines, and data compression strategies for ML use cases
• Strong knowledge of Linux systems, Python, and C++ or similar performance-oriented languages
• Experience operating in hybrid environments: bare metal, HPC, and public cloud (AWS/GCP/Azure)
• Comfortable owning cross-org initiatives and influencing system-level design across autonomy, simulation, and platform teams

Responsibilities

• Own the architecture of Motional’s ML data infrastructure, enabling scalable ingestion, storage, curation, and access for 100+ engineers and researchers across autonomy teams
• Design and evolve infrastructure to support petabyte-scale machine learning workflows, including multimodal perception data, synthetic data, simulation output, and continuous training pipelines
• Architect high-throughput systems for distributed training on large GPU clusters, driving significant improvements in utilization, throughput, and job efficiency
• Establish robust data governance, observability, and retention strategies to ensure compliance, reproducibility, and long-term data utility
• Collaborate cross-functionally with ML engineers, autonomy researchers, data engineers, and DevOps to ensure tight integration between infrastructure and user workflows
• Lead technical strategy and roadmap development for the ML & Data Platform team, incorporating cutting-edge tools and best practices from industry and open source
• Mentor and influence engineers across teams, promoting engineering excellence in distributed systems, ML platforms, and autonomy-scale data management

Preferred Qualifications

• Prior work in robotics, autonomous vehicles, or safety-critical domains strongly preferred
• Experience building or leading infrastructure at a top-tier ML/AI company or AV program
• Background contributing to open-source ML or data infrastructure projects
• Familiarity with ML experiment tracking, model evaluation pipelines, and versioned data systems

Benefits

Medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance