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Machine Learning Infrastructure Jobs in California

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Machine Learning Infrastructure information

What is the difference between Machine Learning Infrastructure vs Data Engineer?

AspectMachine Learning InfrastructureData Engineer
Required CredentialsBachelor's in CS, experience with ML toolsBachelor's in CS, experience with data pipelines
Work EnvironmentFocus on ML systems, cloud platformsData pipelines, database management
Employer & Industry UsageTech companies, AI startupsAny industry with data needs, tech firms
Search & Comparison IntentUnderstanding ML system setupBuilding data pipelines

Machine Learning Infrastructure specialists focus on deploying and maintaining systems that support machine learning models, often working with cloud platforms and ML tools. Data Engineers build and manage data pipelines and databases, supporting data collection and processing. While both roles require technical skills and overlap in data handling, Machine Learning Infrastructure is more centered on ML system deployment, whereas Data Engineers focus on data architecture and pipelines.

What are the typical challenges faced by professionals working in Machine Learning Infrastructure roles?

Professionals in Machine Learning Infrastructure often encounter challenges related to scaling systems to handle large datasets, ensuring model reproducibility, and maintaining efficient workflows for both development and deployment. Collaborating closely with data scientists, software engineers, and DevOps teams is crucial to address issues like version control, resource allocation, and performance optimization. Staying updated on evolving tools and cloud platforms is also essential, as the landscape changes rapidly and impacts system design and integration.

What are the key skills and qualifications needed to thrive in Machine Learning Infrastructure, and why are they important?

To excel in Machine Learning Infrastructure, you need a solid background in computer science, software engineering, and distributed systems, often supported by experience in deploying and scaling machine learning models. Familiarity with cloud platforms (like AWS, GCP, or Azure), containerization tools (such as Docker and Kubernetes), and ML workflow systems (e.g., TensorFlow Extended, MLflow) is crucial. Strong problem-solving skills, collaboration, and the ability to communicate technical concepts effectively help you stand out in this field. These skills ensure scalable, reliable, and efficient deployment of ML solutions, enabling organizations to leverage machine learning at production scale.

What is machine learning infrastructure?

Machine learning infrastructure refers to the combination of hardware, software, platforms, and tools necessary to support the development, training, deployment, and maintenance of machine learning models at scale. This includes computing resources like GPUs and CPUs, data storage systems, workflow orchestration tools, model serving frameworks, and monitoring solutions. The goal of ML infrastructure is to streamline and automate the machine learning lifecycle, enabling data scientists and engineers to build and deploy models more efficiently and reliably.
What are popular job titles related to Machine Learning Infrastructure jobs in California? For Machine Learning Infrastructure jobs in California, the most frequently searched job titles are:
What job categories do people searching Machine Learning Infrastructure jobs in California look for? The top searched job categories for Machine Learning Infrastructure jobs in California are:
Machine Learning Infrastructure jobs near you

Machine Learning Infrastructure Engineer

Mind Robotics

Palo Alto, CA

$126K - $166K/yr

Other

Posted 6 days ago

Job description

Machine Learning Infrastructure Engineer

At Mind Robotics, we're building generalized physical AI—robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments. Our ability to iterate quickly on large-scale models depends on world-class ML infrastructure.

We're looking for a Machine Learning Infrastructure Engineer to build the core systems that enable fast, reliable, and scalable model training—powering everything from experimentation to production deployment.

Responsibilities
  • Design and implement scalable systems for training large ML models
  • Enable efficient workflows for data ingestion, training, and iteration
  • Develop and optimize distributed training systems across hundreds of GPUs
  • Implement strategies for parallelization, sharding, and efficient compute utilization
  • Improve training efficiency through techniques such as attention optimizations, kernel fusion, and memory management
  • Partner closely with modeling teams to accelerate iteration speed and reduce training costs
  • Build internal tools for experiment tracking, monitoring, and debugging
  • Implement systems for tracking training performance, failures, and resource utilization
  • Debug and resolve bottlenecks across the training stack
  • Provide lightweight infrastructure support for deploying and running models in production environments
  • Optimize inference performance and reliability where needed
  • Support core cloud infrastructure needs for training workloads (without heavy DevOps overhead)
  • Manage compute resources efficiently across training jobs
Qualifications
  • Strong experience building infrastructure for large-scale ML training
  • Deep understanding of how modern LLM/VLM systems are trained and scaled
  • Proven experience setting up and scaling distributed training across hundreds of GPUs
  • Strong understanding of parallelization strategies (data, model, pipeline parallelism)
  • Strong proficiency in Python programming
  • Expert-level proficiency in PyTorch and/or JAX
  • Strong understanding of techniques like attention optimization, kernel fusion, and efficient memory usage
Nice to Have
  • Experience supporting inference systems in production
  • Familiarity with robotics or embodied AI workloads
  • Experience building tools for experiment management and researcher productivity

Apply