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Machine Learning Infrastructure Engineer Jobs in San Diego, CA

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize and integrate search ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize and integrate search ...

Apple Inc.

Machine Learning Engineer- GenAI

San Diego, CA · On-site

$150.40 - $277.60/hr

... engineer to join our team. You will help design and implement our machine learning strategy to the ... Familiarity with distributed computing, cloud infrastructure, and orchestration tools, such as ...

Qualcomm

Senior Engineer - Machine Learning

San Diego, CA · On-site

$140.80 - $211.20/hr

Company Qualcomm Incorporated Job Area Engineering Group, Engineering Group > Machine Learning ... This role focuses on model development, inference optimization, and scalable ML infrastructure ...

Qualcomm

Senior Engineer - Machine Learning

San Diego, CA · On-site

$110K - $152K/yr

Engineering Group, Engineering Group > Machine Learning Engineering General Summary: We are seeking ... Collaborate with platform and infrastructure teams for scalable deployment Data & Pipeline ...

Qualcomm

Senior Engineer - Machine Learning

San Diego, CA · On-site

$110K - $152K/yr

Engineering Group, Engineering Group > Machine Learning Engineering General Summary: We are seeking ... Collaborate with platform and infrastructure teams for scalable deployment Data & Pipeline ...

AbbVie

Machine Learning Engineer

San Diego, CA · On-site +1

$109K/yr

Design, train, evaluate, and refine machine learning models with minimal supervision, applyingsound statistical and engineering practices * Implement ML solutions that can be deployed into production ...

AbbVie

Machine Learning Engineer

San Diego, CA · On-site +1

Design, train, evaluate, and refine machine learning models with minimal supervision, applyingsound statistical and engineering practices * Implement ML solutions that can be deployed into production ...

Icwgroup

Machine Learning Engineer

San Diego, CA · Hybrid

PURPOSE OF THE JOB The Machine Learning Engineer (MLE) selected for this role at ICW Group will build and deploy high-performing machine learning models across the enterprise. As an MLE on the ...

General Atomics

Machine Learning Engineer II

Poway, CA · On-site

$98K - $171K/yr

We have an exciting opportunity for a Machine Learning Engineer in Poway, CA. The Autonomy and Artificial Intelligence Solutions Software group is charted to develop and deploy end-to-end autonomous ...

Apple Inc.

Machine Learning Engineer- Gen AI

San Diego, CA · On-site

$142.30 - $214.30/hr

... different engineering and operations teams; our team leads development of machine learning ... Familiarity with distributed computing, cloud infrastructure, and orchestration tools, such as ...

General Atomics

Machine Learning Engineer III

Poway, CA · On-site

$116K - $208K/yr

May substitute equivalent machine learning engineer experience in lieu of education. * Must have an advanced understanding of machine learning concepts, principles, and theory. * Demonstrates the ...

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All JobsMachine Learning Infrastructure Engineer JobsMachine Learning Infrastructure Engineer Jobs in San Diego, CA

Machine Learning Infrastructure Engineer information

See San Diego, CA salary details

$49.4K

$134.9K

$193.2K

How much do machine learning infrastructure engineer jobs pay per year?

As of Aug 22, 2026, the average yearly pay for machine learning infrastructure engineer in San Diego, CA is $134,908.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,100.00 and $149,700.00 per year, depending on experience, location, and employer.

What is a machine learning infrastructure engineer?

A Machine Learning Infrastructure Engineer designs, builds, and maintains the systems that support the development and deployment of machine learning models. This includes managing data pipelines, optimizing model training and inference, and ensuring scalability and reliability in production environments. They work closely with data scientists, ML engineers, and DevOps teams to create efficient workflows and infrastructure. Key technologies often include cloud platforms, containerization, orchestration tools, and distributed computing frameworks.

What are the key skills and qualifications needed to thrive as a machine learning infrastructure engineer?

To thrive as a Machine Learning Infrastructure Engineer, you need a strong background in computer science, cloud computing, distributed systems, and experience with machine learning frameworks, often supported by a degree in a related field. Familiarity with tools such as Docker, Kubernetes, Terraform, as well as cloud platforms like AWS, GCP, or Azure, and certifications in cloud or DevOps technologies are highly valued. Strong problem-solving abilities, effective communication, and collaboration skills help engineers work seamlessly with data scientists and cross-functional teams. These skills are essential to design, implement, and maintain robust, scalable infrastructure that enables efficient machine learning development and deployment.

What are some common challenges faced by machine learning infrastructure engineers, and how can these be addressed on the job?

Machine Learning Infrastructure Engineers often face challenges such as ensuring infrastructure scalability, managing resource allocation, and maintaining system reliability while supporting rapid experimentation by data science teams. Balancing the needs for flexibility in research environments with production-grade stability requires a deep understanding of both engineering best practices and the unique requirements of machine learning workflows. Collaboration with data scientists, clear communication about infrastructure capabilities, and staying current with fast-evolving technologies are key strategies for success. Most companies encourage ongoing learning and provide opportunities to contribute to architecture decisions, which makes this a rewarding environment for problem-solvers and innovators.

What are popular job titles related to Machine Learning Infrastructure Engineer jobs in San Diego, CA?

For Machine Learning Infrastructure Engineer jobs in San Diego, CA, the most frequently searched job titles are:

What job categories do people searching Machine Learning Infrastructure Engineer jobs in San Diego, CA look for?

The top searched job categories for Machine Learning Infrastructure Engineer jobs in San Diego, CA are:

What cities near San Diego, CA are hiring for Machine Learning Infrastructure Engineer jobs?

Cities near San Diego, CA with the most Machine Learning Infrastructure Engineer job openings:

Machine Learning Infrastructure Engineer jobs near you
Infographic showing various Machine Learning Infrastructure Engineer job openings in San Diego, CA as of August 2026, with employment types broken down into 94% Full Time, 2% Part Time, 2% Temporary, and 2% Contract. Highlights an 90% In-person, 3% Hybrid, and 7% Remote job distribution, with an average salary of $134,908 per year, or $64.9 per hour.

Senior Machine Learning Infrastructure Engineer, Simulation

Waymo

San Diego, CA

$115K - $156K/yr

Full-time

Re-posted 21 days ago

Job description

The Simulation ML Infrastructure team builds scalable AI/ML infrastructure to accelerate the Simulator team in sustainably innovating and building state of the art simulations of realistic environments for the testing and training of the Waymo Driver. To increase the fidelity and steerability of the simulations, we employ large foundation models trained on massive datasets to model the real world, including but not limited to, realistic agents (vehicles, pedestrians, cyclists, motorcyclists etc.), roads, traffic control systems, and weather etc.

We seek an experienced Senior Machine Learning Infrastructure Engineer to lead the development of advanced AI/ML infrastructure for multi-billion parameter foundation models in ML accelerator-friendly simulations. Your expertise in massive model scaling, ML accelerators, and distributed training will be required for designing and scaling our systems.

This role reports to an Engineering Manager.

You will:

  • Be part of a world-class, high-performing research engineering team to advance the state of the art of ultra realistic multi-agent simulations using foundation models.

  • Collaborate closely with the core Google DeepMind and Waymo Realism Modeling teams in London, and Waymo Oxford to use the large models to improve sim realism.

  • Provide deep technical leadership on large-scale ML model architectures, especially for autonomous vehicle models. Work at the intersection of data engineering, model development, and deployment, and provide guidance on architectural decisions and technical directions. Own large, complex systems, driving architectures that meet technical and business objectives.

  • Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation and model training.

  • Collaborate cross-functionally to derive performance and system-level requirements for large ML systems. Translate product/business goals into measurable technical deliverables, ensuring system component alignment.

  • Mentor junior engineers, growing their expertise and fostering a collaborative culture.

You have:

  • BS in Computer Science, Robotics, similar technical field of study, or equivalent practical experience

  • 5+ years of professional software engineering experience, with at least 3 years in machine learning infrastructure such as developing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.

We prefer:

  • 10+ years of professional software engineering experience, with at least 5 years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.

  • Solid experience in the development and optimization of machine learning infrastructure tools like DeepSpeed, PyTorch, TensorFlow, or similar frameworks.

  • Strong expertise in distributed training techniques, including gradient sharding and optimization strategies for scaling large models across ML accelerator profiling tools to uncover performance bottlenecks.

  • Deep understanding of state-of-the-art machine learning models such as auto-regressive transformers and familiarity with custom-kernels for diverse h/w compute based efficiency.

  • Excellent communication skills, both verbal and written, with the ability to translate complex technical concepts for a broad audience.

  • Practical familiarity in Autonomous Driving, Simulations, and ML accelerators is a plus.

Apply