ML Infrastructure Software Engineer, Nuro

• Experience: 3+ years of professional experience in ML Infrastructure, Backend Platform Engineering, or Distributed Systems
• Resource Provisioning: Deep familiarity with modern Infrastructure-as-Code and provisioning tools such as Terraform, Pulumi, or Crossplane
• Workload Scheduling: Hands-on experience building or managing large-scale orchestrators for compute-heavy workloads (e.g., Kubernetes, KubeRay, Ray, Slurm, or Volcano)
• Distributed Data Processing: Proficiency in at least one distributed processing framework, such as Apache Spark or Apache Beam, for large-scale data extraction and transformation
• Feature Management: Experience implementing or maintaining feature stores and caching layers (e.g., Feast, Hopsworks, or Redis-based custom caching)
• Systems Design: A strong understanding of distributed systems, networking, and storage bottlenecks in the context of high-performance computing

Desirable

• Nuro is seeking a Software Engineer with expertise in large-scale infrastructure, workload orchestration, and data processing to join our ML Infrastructure team
• In this role, you will focus on building and evolving the core platform that provides researchers and engineers with seamless access to compute and data resources
• You will be responsible for executing the technical strategy for automated resource provisioning, high-performance workload scheduling, and efficient feature management to accelerate the Nuro Driver™ development lifecycle
• You will build the foundation that powers Nuro’s model development from experimentation to production
• Resource Provisioning & IaC: Scaling automated infrastructure-as-code (IaC) pipelines to manage thousands of GPU/CPU nodes across diverse environments
• Intelligent Scheduling: Designing and optimizing workload orchestration to maximize hardware utilization, minimize job wait times, and handle massive-scale distributed training
• Data & ETL: Designing robust pipelines for the extraction and transformation of petabyte-scale sensor and telemetry data into ML-ready formats
• Feature Management: Implementing robust feature caching and storage solutions to reduce redundant computations and ensure low-latency access to pre-computed features
• Platform Abstraction: Contributing to a unified ML platform that abstracts complex cloud infrastructure for end-users