The opportunity
Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy…
What you'll do
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.
Experience: 4+ years of professional experience in ML Infrastructure, Backend: Platform Engineering, or Distributed Systems.
What they're looking for
- 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).
