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Ml Infrastructure Jobs (NOW HIRING)

ML Infrastructure Engineer

San Francisco, CA · On-site

$126K - $166K/yr

Build intuitive internal tools and abstractions that make complex infrastructure easy for engineers to use. * Lead technical and commercial discussions with cloud and ML compute providers, including ...

AI/ML Infrastructure Engineer

San Francisco, CA · On-site

$126K - $166K/yr

The AI Infrastructure team at Zensors builds the engine that powers our visual sensing platform. We ... As a Machine Learning Engineer in ML Runtime & Optimization , you will develop technologies to ...

Develop and maintain infrastructure that supports efficient ML operations, including data pipelines, model evaluations, deployments, and training at scale. * Collaborate closely with ML engineers ...

ML Infrastructure Engineer

Sunnyvale, CA · Hybrid

$119K - $187K/yr

Hands-on experience in ML platforms * Experience with GPU/TPU optimizations * Experience with Ray framework * Experience with Kubernetes at Scale * Experience infrastructure applications or similar ...

ML Infrastructure Engineer

Sunnyvale, CA · On-site

$119K - $187K/yr

Hands-on experience in ML platforms * Experience with GPU/TPU optimizations * Experience with Ray framework * Experience with Kubernetes at Scale * Experience infrastructure applications or similar ...

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Ml Infrastructure information

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$46.5K

$127.1K

$182K

How much do ml infrastructure jobs pay per year?

As of Jul 14, 2026, the average yearly pay for ml infrastructure in the United States is $127,066.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,500.00 and $141,000.00 per year, depending on experience, location, and employer.

What are some common challenges faced by professionals working in ML Infrastructure roles?

Professionals in ML Infrastructure often encounter challenges related to scaling systems to handle large volumes of data, ensuring reliable deployment pipelines, and maintaining reproducibility across different environments. They must also collaborate closely with data scientists and engineers to streamline workflows and address issues like version control and model monitoring. Staying updated with rapidly evolving tools and best practices is essential, and balancing stability with innovation is a frequent aspect of the role.

What is the difference between Ml Infrastructure vs Data Engineer?

AspectML InfrastructureData Engineer
Required CredentialsBachelor's in CS, Data Science, or related; knowledge of cloud platformsBachelor's in CS, Software Engineering, or related; experience with databases and ETL tools
Work EnvironmentFocus on deploying and maintaining ML systems, cloud environments, and infrastructure toolsDesigning, building, and managing data pipelines and storage solutions
Industry UsageUsed in AI/ML teams to support model deployment and scalabilityUsed across data-driven organizations for data management and analytics

ML Infrastructure specialists focus on deploying, scaling, and maintaining machine learning systems and infrastructure, while Data Engineers primarily build and manage data pipelines and storage solutions. Both roles require technical skills and often collaborate, but their core responsibilities differ in focus and tools used.

What are the key skills and qualifications needed to thrive as an ML Infrastructure Engineer, and why are they important?

To thrive as an ML Infrastructure Engineer, you need a strong background in software engineering, cloud computing, and machine learning concepts, often supported by a degree in computer science or a related field. Proficiency with containerization tools (like Docker and Kubernetes), cloud platforms (such as AWS, GCP, or Azure), and CI/CD systems is critical. Excellent problem-solving, collaboration, and communication skills help you efficiently work with data scientists and DevOps teams. These skills and qualities are vital for building scalable, reliable ML systems that support rapid experimentation and deployment in production environments.

What is ML Infrastructure?

ML Infrastructure refers to the underlying systems, tools, and processes that enable the development, deployment, and scaling of machine learning models. This includes data storage and management, computing resources, model training and serving environments, monitoring, and automation tools. ML Infrastructure ensures that data scientists and engineers can efficiently build, test, and maintain machine learning applications in a reliable and reproducible manner. It is a crucial foundation for organizations looking to operationalize AI and machine learning solutions at scale.
More about Ml Infrastructure jobs
What cities are hiring for Ml Infrastructure jobs? Cities with the most Ml Infrastructure job openings:
What states have the most Ml Infrastructure jobs? States with the most job openings for Ml Infrastructure jobs include:
Infographic showing various Ml Infrastructure job openings in the United States as of July 2026, with employment types broken down into 95% Full Time, 3% Part Time, and 2% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $127,066 per year, or $61.1 per hour.
Software Engineer, ML Infrastructure

Software Engineer, ML Infrastructure

Nuro

Mountain View, CA

$160K - $240K/yr

Full-time

Re-posted 9 days ago


Job description

Who We Are

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 platform: self-driving for all roads and all rides.
Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driverâ„¢, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles.
With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected.
Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors

About the Role

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 Driverâ„¢ development lifecycle.

About the Work

You will build the foundation that powers Nuro's model development from experimentation to production. Key responsibilities include:

  • 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.

About You

  • 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, GCP, or Azure).

At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $160,360 and $240,540 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package.

At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics.