> Markdown version of [/jobs/ext/600182-software-engineer-ml-infrastructure](https://www.wearedevelopers.com/jobs/ext/600182-software-engineer-ml-infrastructure). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer, ML Infrastructure - **Company:** Nuro Inc. - **Location:** United States - **Experience:** Experienced - **Salary:** $160,360.0 - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Cloud Engineering, Computer Clusters, Extract Transform Load (ETL), Device Drivers, Distributed Computing Environment, Distributed Systems, Redis, Azure Machine Learning, Ceph (Software), Pulumi, Data Processing, Google Cloud, High Performance Computing, Apache Spark, Backend, Kubernetes, Slurm, Machine Learning Operations, Terraform, Apache Beam, Nvme - **Published:** June 23, 2026 - **Apply:** https://www.dice.com/job-detail/26b78cde-ccd7-461e-9f73-c67419c99f78 ## About the Role * 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. Bonus Points * Active contributor to open-source projects in the MLOps or Cloud-Native ecosystem (e.g., CNCF, Ray, or Kubeflow communities). * Experience with high-performance storage systems (e.g., Lustre, Ceph, or specialized NVMe caching) for ML data loading. * Knowledge of cost-optimization strategies for large-scale GPU clusters in public clouds (AWS, Google Cloud Platform, or Azure). ## Description 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., * 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. ## Related Videos - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices](https://www.wearedevelopers.com/videos/1582-llmops-driven-fine-tuning-evaluation-and-inference-with-nvidia-nim-nemo-microservices) - [Accelerating Authentication Architecture: Taking Passwordless to the Next Level](https://www.wearedevelopers.com/videos/733-accelerating-authentication-architecture-taking-passwordless-to-the-next-level) - [The state of MLOps - machine learning in production at enterprise scale](https://www.wearedevelopers.com/videos/369-the-state-of-mlops-machine-learning-in-production-at-enterprise-scale) - [Event based cache invalidation in GraphQL](https://www.wearedevelopers.com/videos/433-event-based-cache-invalidation-in-graphql) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)