AI Platform Engineer Jobs in California
AI Platform Engineer jobs in California represent one of the most active and competitive markets in the country, concentrated in cloud infrastructure, enterprise AI, and machine learning operations roles at every level from associate to principal engineer. The heaviest hiring is in San Francisco, San Jose, and Los Angeles, where companies like Google, Meta, and Salesforce maintain large engineering organizations with dedicated AI platform teams. The most in-demand specialties are MLOps pipeline development, LLM infrastructure, and distributed systems engineering. Find a role that fits below and apply directly.
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Research Scientist, Artificial Intelligence Responsibilities:
- Lead the design and execution of TPU performance optimization research, including kernel development, memory optimization, and compute efficiency improvements
- Develop and optimize Pallas kernels for large-scale model training and inference on TPU architectures
- Drive model optimization techniques including Mixture of Experts (MoE), tensor parallelism, pipeline parallelism, and other distributed training strategies
- Optimize first party models within Meta's native PyTorch stack, ensuring efficient integration with XLA compilation and TPU execution
- Identify and resolve complex technical challenges in model training efficiency, inference latency, and system reliability that require novel approaches
- Define and drive multi-quarter research roadmaps for TPU optimization, aligning project milestones with broader organizational goals
- Establish rigorous experimentation frameworks for performance benchmarking, including metric selection, profiling methodology, and data-driven optimization decisions
- Translate research findings into production-ready optimizations by collaborating with engineering teams on deployment pipelines and reliability at scale
- Communicate research findings and technical trade-offs clearly through publications, design documents, and presentations to both technical and non-technical audiences
- Mentor other researchers and engineers on TPU optimization techniques, providing structured feedback on technical direction and experimental rigor
Minimum Qualifications:
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- 8+ years of experience in machine learning systems, model optimization, or high-performance computing research
- Experience with TPU architecture and performance optimization, including profiling, kernel development, and memory management
- Experience with XLA compilation, graph optimization, and low-level performance tuning for accelerator hardware
- Experience developing and optimizing large-scale distributed training systems, including parallelism strategies such as data, tensor, and pipeline parallelism
- Experience with PyTorch and its integration with accelerator backends
- Experience communicating complex technical findings in writing, including technical reports, design documents, or peer-reviewed publications
Preferred Qualifications:
- Experience developing custom kernels using Pallas or similar kernel authoring frameworks for TPU or GPU
- Demonstrated track record of transitioning performance research into deployed systems used at significant scale
- PhD in Computer Science, Machine Learning, Computer Architecture, or a related technical field, or equivalent depth of research experience
- Publication record in systems for ML venues such as MLSys, OSDI, SOSP, or related AI conferences such as NeurIPS, ICML, or ICLR
- Experience with Mixture of Experts (MoE) architectures and their optimization for efficient training and inference
- Experience optimizing production-scale models with billions of parameters
About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.
$184,000/year to $257,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.
Research Scientist, Artificial Intelligence Responsibilities:
- Lead the design and execution of TPU performance optimization research, including kernel development, memory optimization, and compute efficiency improvements
- Develop and optimize Pallas kernels for large-scale model training and inference on TPU architectures
- Drive model optimization techniques including Mixture of Experts (MoE), tensor parallelism, pipeline parallelism, and other distributed training strategies
- Optimize first party models within Meta's native PyTorch stack, ensuring efficient integration with XLA compilation and TPU execution
- Identify and resolve complex technical challenges in model training efficiency, inference latency, and system reliability that require novel approaches
- Define and drive multi-quarter research roadmaps for TPU optimization, aligning project milestones with broader organizational goals
- Establish rigorous experimentation frameworks for performance benchmarking, including metric selection, profiling methodology, and data-driven optimization decisions
- Translate research findings into production-ready optimizations by collaborating with engineering teams on deployment pipelines and reliability at scale
- Communicate research findings and technical trade-offs clearly through publications, design documents, and presentations to both technical and non-technical audiences
- Mentor other researchers and engineers on TPU optimization techniques, providing structured feedback on technical direction and experimental rigor
Minimum Qualifications:
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- 8+ years of experience in machine learning systems, model optimization, or high-performance computing research
- Experience with TPU architecture and performance optimization, including profiling, kernel development, and memory management
- Experience with XLA compilation, graph optimization, and low-level performance tuning for accelerator hardware
- Experience developing and optimizing large-scale distributed training systems, including parallelism strategies such as data, tensor, and pipeline parallelism
- Experience with PyTorch and its integration with accelerator backends
- Experience communicating complex technical findings in writing, including technical reports, design documents, or peer-reviewed publications
Preferred Qualifications:
- Experience developing custom kernels using Pallas or similar kernel authoring frameworks for TPU or GPU
- Demonstrated track record of transitioning performance research into deployed systems used at significant scale
- PhD in Computer Science, Machine Learning, Computer Architecture, or a related technical field, or equivalent depth of research experience
- Publication record in systems for ML venues such as MLSys, OSDI, SOSP, or related AI conferences such as NeurIPS, ICML, or ICLR
- Experience with Mixture of Experts (MoE) architectures and their optimization for efficient training and inference
- Experience optimizing production-scale models with billions of parameters
About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.
$184,000/year to $257,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.
See All 165+ AI Platform Engineer Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
Find AI Platform Engineer JobsAI Platform Engineer Jobs by City in California
Where California roles are concentrated, by current openings.
AI Platform Engineer Job Market in California
A snapshot from current California openings, updated as new roles post.
Who's Hiring
- TikTok12
- Capital One10
- Innovaccer5
- GEICO4
- Google4
Top Industries Hiring
- Technology & Software22
- Science & Research4
- Banking & Financial Services4
- Artificial Intelligence3
- Insurance2
What California Employers Look For
The qualifications that appear most often in AI platform engineer jobs across California.
- Bachelor's or master's degree in computer science, software engineering, or a related technical field
- Hands-on experience building and maintaining ML pipelines using tools such as Kubeflow, MLflow, or Ray
- Proficiency with cloud platforms including Google Cloud, AWS, or Azure in production AI environments
- Strong programming skills in Python and familiarity with containerization using Docker and Kubernetes
- Experience with large-scale distributed systems, data engineering, and model deployment infrastructure
- Familiarity with CI/CD practices and infrastructure-as-code tools such as Terraform or Pulumi
AI Platform Engineer Jobs in California: Frequently Asked Questions
How do you become a ai platform engineer in California?
Most California employers expect a bachelor's degree in computer science, software engineering, or a related discipline, though a master's degree is increasingly common at larger tech firms. There is no state-issued license for this role in California. Candidates strengthen their profile by earning cloud certifications from providers like Google or AWS, building a portfolio of deployed ML systems, and gaining hands-on experience through internships or open-source contributions to AI infrastructure projects.
How much do AI platform engineers make in California?
AI platform engineers in California earn a median of about $134,440 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $58,340 for the lowest 10% to over $222,690 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire ai platform engineers in California?
Employers hiring ai platform engineers in California right now include TikTok, Capital One, and Innovaccer, based on current listings on Migrate Mate as of September 2026. California's concentration of cloud-native technology companies and enterprise software headquarters means demand runs consistently across both large established firms and well-funded growth-stage companies throughout the Bay Area and Los Angeles.
Which California cities have the most ai platform engineer jobs?
San Francisco, San Jose, and Santa Clara have the most ai platform engineer openings in California. San Francisco and San Jose dominate because they sit at the center of the Bay Area technology ecosystem, home to the headquarters of major cloud, AI, and enterprise software companies, while Los Angeles has grown significantly as a hub for media-tech, fintech, and consumer AI firms driving their own platform infrastructure needs.
Are there remote ai platform engineer jobs in California?
Yes, and more than most fields. About 62% of ai platform engineer openings tied to California are remote or hybrid as of September 2026, reflecting how much of this work involves cloud systems and code rather than on-site hardware. Infrastructure design, pipeline development, and model monitoring tasks are the most commonly performed remotely, though some roles at larger firms require on-site collaboration for security or compliance reasons.
How can I get hired as a ai platform engineer in California with little or no experience?
The most realistic entry path is moving from a software engineering or data engineering role into AI platform work by picking up MLOps tooling on the job or through project work. Large California technology employers often post associate or junior platform engineer roles that accept candidates with cloud fundamentals and Python skills over deep ML experience. Building and publishing an end-to-end ML pipeline project on a public repository, earning a Google Cloud or AWS certification, and targeting companies with established new-grad programs in the Bay Area gives candidates without a formal AI background a concrete edge.
Where can I find and apply to ai platform engineer jobs in California?
You can find and apply to ai platform engineer jobs in California on Migrate Mate, which lists current California openings across the state. Find the roles that fit your experience and location and apply directly to the employers posting them.
See All 165+ AI Platform Engineer Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
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