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Particle Physics Jobs in California (NOW HIRING)

TAE

Accelerator Physicist - Medical Beams

Irvine, CA · On-site

$140K - $160K/yr

A successful candidate would have strong background in physics and technology of particle accelerators, nuclear physics, plasma physics, or related field, coupled with solid experience in designing ...

Causal Labs

Deployment Strategist

San Francisco, CA · On-site

$96K - $111K/yr

Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.

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All JobsParticle Physics JobsParticle Physics Jobs in California

Particle Physics information

See California salary details

$10.9K

$60.4K

$93.3K

How much do particle physics jobs pay per year?

As of Aug 11, 2026, the average yearly pay for particle physics in California is $60,359.00, according to ZipRecruiter salary data. Most workers in this role earn between $45,400.00 and $63,700.00 per year, depending on experience, location, and employer.

What is the difference between Particle Physics vs Nuclear Physics?

AspectParticle PhysicsNuclear Physics
Required CredentialsPhysics degree, PhD often preferred, specialized training in subatomic particlesPhysics or nuclear engineering degree, often with specialized nuclear coursework
Work EnvironmentResearch labs, particle accelerators, universitiesNuclear reactors, research facilities, laboratories
Industry UsageFundamental research, CERN, particle detector developmentNuclear energy, medical imaging, nuclear safety

Particle Physics focuses on understanding the fundamental particles and forces of the universe, often working with large accelerators like CERN. Nuclear Physics studies the properties and reactions of atomic nuclei, with applications in energy and medicine. While both fields require a physics background, their research environments and industry applications differ significantly.

What do particle physicists do?

Particle physicists study the fundamental particles that make up matter and the forces that govern their interactions. They conduct experiments using particle accelerators, develop theoretical models, and analyze data to uncover the basic building blocks of the universe. Their work helps us understand phenomena such as the origin of mass, the nature of dark matter, and the fundamental laws of physics. Particle physicists often collaborate in large international teams and may contribute to breakthroughs in technology and medicine through their research.

What can I do with a degree in particle physics?

A degree in particle physics prepares individuals for careers in research, academia, and industry, including roles such as research scientist, data analyst, or technical consultant. Graduates often work in laboratories, universities, or technology companies, utilizing skills in data analysis, programming, and experimental design.

What are the key skills and qualifications needed to thrive as a particle physicist, and why are they important?

To thrive as a Particle Physicist, you need a strong background in physics and mathematics, usually demonstrated by a Ph.D. in particle physics or a closely related field. Proficiency with programming languages (such as Python or C++), data analysis tools, and experience with particle detectors or accelerator systems is typically required. Critical thinking, problem-solving, and effective collaboration are vital soft skills for designing experiments and interpreting complex data. These skills and qualities are essential for advancing scientific understanding and contributing to collaborative research in a highly technical and innovative field.

What are some common challenges faced by particle physicists in experimental research settings?

Particle physicists working in experimental settings often face challenges such as managing vast amounts of complex data from particle detectors and collaborating within large, international teams. The work frequently involves troubleshooting sophisticated equipment, adhering to strict safety protocols, and adapting to rapidly evolving technologies. Additionally, long-term experiments may require patience and persistence due to the lengthy data collection and analysis phases. Effective communication and strong teamwork skills are essential, as findings must be coordinated and shared with global collaborators.

Is particle physics a good career?

Particle physics is a specialized field that involves research at universities, laboratories, and research institutions, often requiring advanced degrees such as a Ph.D. Skills in mathematics, programming, and data analysis are essential. Careers can be competitive and may involve long hours, but they offer opportunities for contributing to fundamental scientific knowledge and technological advancements.
What are the most commonly searched types of Particle Physics jobs in California? The most popular types of Particle Physics jobs in California are:
What are popular job titles related to Particle Physics jobs in California? For Particle Physics jobs in California, the most frequently searched job titles are:
What job categories do people searching Particle Physics jobs in California look for? The top searched job categories for Particle Physics jobs in California are:
What cities in California are hiring for Particle Physics jobs? Cities in California with the most Particle Physics job openings:
Particle Physics jobs near you
Infographic showing various Particle Physics job openings in California as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 19% Part Time, 2% Temporary, and 2% Contract. Highlights an 81% Physical, 2% Hybrid, and 17% Remote job distribution, with an average salary of $60,359 per year, or $29 per hour.

Member of Technical Staff - Training Infrastructure

Causal Labs

San Francisco, CA • On-site

Full-time

Re-posted 20 days ago

Job description

Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.
To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because physical systems, unlike text or images, are governed by verifiable cause and effect. We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.
Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.
We look for infrastructure engineers who are excited to tackle unsolved problems. Training an LPM means scaling novel architectures over multimodal physical data - a problem where the playbooks from language and vision only partially apply. Your mission is to make large-scale training fast, efficient, and reliable, so that every GPU cycle accelerates research progress.
Responsibilities
  • Design, implement, and optimize distributed training systems that scale across thousands of GPUs
  • Research and test parallelization strategies and numerical precision trade-offs across model scales, including for architectures that don't map cleanly onto existing LLM training stacks
  • Analyze, profile, and debug low-level GPU operations to maximize throughput and hardware utilization
  • Build reusable frameworks for checkpointing, fault tolerance, and reproducibility that stay robust under rapid research iteration
  • Collaborate with researchers to bring novel model architectures from prototype to full scale
  • Stay up-to-date on research to bring new ideas to work

What we're looking for
We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
  • Demonstrated proficiency with distributed training frameworks and techniques (e.g. FSDP, DeepSpeed, Megatron, Pytorch, JAX/XLA) to train large foundation models
  • Strong grasp of state-of-the-art techniques for optimizing training workloads: parallelism strategies, memory optimization, mixed precision, communication overlap
  • Ability to profile and debug performance in complex codebases, from framework internals down to kernels and collectives
  • Deep understanding of deep learning frameworks (e.g. PyTorch, JAX) and their underlying system architectures
  • Bonus: contributions to open-source ML infrastructure (e.g. PyTorch, Megatron-LM, DeepSpeed, XLA)

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