Senior Software Engineer; ML Infrastructure

Requirements

• Experience:
  
  5+ years of professional experience in ML Infrastructure, Backend Platform Engineering, or Distributed Systems
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• Resource Provisioning:
  Deep familiarity with modern Infrastructure-as-Code and provisioning tools such as Terraform, Pulumi, or Crossplane
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• Workload Scheduling:
  Hands-on experience building or managing large-scale orchestrators for compute-heavy workloads (e.g., Kubernetes, Kube Ray, Ray, Slurm, or Volcano)
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• 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
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• Feature Management:
  Experience implementing or maintaining feature stores and caching layers (e.g., Feast, Hopsworks, or Redis-based custom caching)
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• Systems Design: A strong understanding of distributed systems, networking, and storage bottlenecks in the context of high-performance computing
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• (Desirable) Active contributor to open-source projects in the MLOps or Cloud-Native ecosystem (e.g., CNCF, Ray, or Kubeflow communities)
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• (Desirable) Experience with high-performance storage systems (e.g., Lustre, Ceph, or specialized NVMe caching) for ML data loading
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• (Desirable) Knowledge of cost-optimization strategies for large-scale GPU clusters in public clouds (AWS, GCP, or Azure)

• Nuro is seeking a Software Engineer with expertise in large-scale infrastructure, workload orchestration, and data processing to join our ML Infrastructure team
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• 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
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• 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
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• You will build the foundation that powers Nuro’s model development from experimentation to production.
  
  Key responsibilities include:
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• Resource Provisioning & IaC:
  Scaling automated infrastructure-as-code (IaC) pipelines to manage thousands of GPU/CPU nodes across diverse environments
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• Intelligent Scheduling:
  Designing and optimizing workload orchestration to maximize hardware utilization, minimize job wait times, and handle massive-scale distributed training
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• Data & ETL:
  Designing robust pipelines for the extraction and transformation of petabyte-scale sensor and telemetry data into ML-ready formats
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• Feature Management:
  Implementing robust feature caching and storage solutions to reduce redundant computations and ensure low-latency access to pre-computed features
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• Platform Abstraction:
  Contributing to a unified ML platform that abstracts complex cloud infrastructure for end-users