Data & ML Infrastructure Architect

Position Overview

• Location Type: Hybrid
• Employment Type: Full-time
• Salary: Not specified
• Description: We’re seeking a Machine Learning & Data Infrastructure Architect to lead the technical vision and architecture for the systems that power our entire machine learning lifecycle—from data ingestion and storage to model training, evaluation, and deployment. This is a mission-critical leadership role within the ML & Data Platform team, shaping the infrastructure that supports terabytes of daily sensor data and petabyte-scale datasets essential for autonomous vehicle development. This role is ideal for a senior technologist with a deep background in ML systems and data architecture, who thrives on building for scale, performance, and engineering excellence.

Requirements

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

Responsibilities

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

Bonus Points

• 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.