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Linear Accelerator Engineer Jobs in California (NOW HIRING)

Leidos

Algorithm Software Engineer

El Cajon, CA · On-site

$140K - $220K/yr

Optimizing C code for DSP applications (i.e. leveraging SIMD, VLIW, or hardware accelerators) and ... Understanding of linear algebra and probability * Strong understanding of basic signal processing ...

Adobe

Director, ML Services Engineering

San Jose, CA · On-site

AI Platform, and adjacent Adobe orgs - to negotiate shared infrastructure, accelerator capacity ... linear headcount growth. * Define the operating rhythm -goal-setting, exec reviews, and engineering ...

Leidos

Algorithm Software Engineer

Rancho Santa Fe, CA · On-site

$140K - $220K/yr

Optimizing C code for DSP applications (i.e. leveraging SIMD, VLIW, or hardware accelerators) and ... Understanding of linear algebra and probability * Strong understanding of basic signal processing ...

Leidos

Algorithm Software Engineer

La Jolla, CA · On-site

$140K - $220K/yr

Optimizing C code for DSP applications (i.e. leveraging SIMD, VLIW, or hardware accelerators) and ... Understanding of linear algebra and probability * Strong understanding of basic signal processing ...

Leidos

Algorithm Software Engineer

San Diego, CA · On-site

Optimizing C code for DSP applications (i.e. leveraging SIMD, VLIW, or hardware accelerators) and ... Understanding of linear algebra and probability * Strong understanding of basic signal processing ...

Leidos

Algorithm Software Engineer

National City, CA · On-site

$140K - $220K/yr

Optimizing C code for DSP applications (i.e. leveraging SIMD, VLIW, or hardware accelerators) and ... Understanding of linear algebra and probability * Strong understanding of basic signal processing ...

Leidos

Algorithm Software Engineer

San Diego, CA

Optimizing C code for DSP applications (i.e. leveraging SIMD, VLIW, or hardware accelerators) and ... Understanding of linear algebra and probability * Strong understanding of basic signal processing ...

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Linear Accelerator Engineer information

See California salary details

$32.6K

$88K

$140.1K

How much do linear accelerator engineer jobs pay per year?

As of Sep 15, 2026, the average yearly pay for linear accelerator engineer in California is $88,015.00, according to ZipRecruiter salary data. Most workers in this role earn between $65,600.00 and $107,600.00 per year, depending on experience, location, and employer.

What is a linear accelerator engineer?

A Linear Accelerator Engineer is responsible for the installation, maintenance, and repair of linear accelerators (LINACs), which are used in medical and industrial applications. They ensure the safe and accurate operation of these machines, often working with oncologists, medical physicists, and radiation therapists in healthcare settings. Their duties include troubleshooting technical issues, calibrating equipment, and performing routine servicing to comply with safety regulations. Strong knowledge of electronics, radiation physics, and mechanical systems is essential for this role.

What does a linear accelerator engineer do?

As a Linear Accelerator Engineer, your daily tasks often include performing preventive maintenance, troubleshooting technical issues, calibrating equipment to ensure precise radiation delivery, and upgrading system components. You’ll commonly collaborate with radiation oncologists, medical physicists, and clinical engineers to guarantee patient safety and optimal system performance. Regular documentation of maintenance activities and strict adherence to safety and compliance standards are integral to the role. This position provides a dynamic and impactful work environment, with opportunities to contribute to advancements in medical technology and patient care.

What are the key skills and qualifications needed to thrive as a linear accelerator engineer?

To thrive as a Linear Accelerator Engineer, you need strong expertise in electronics, mechanical systems, and physics, typically backed by a degree in engineering or a related field. Familiarity with medical linear accelerator equipment, radiation safety protocols, and tools such as oscilloscopes and specialized calibration software is required, and certifications like Certified Medical Equipment Manager (CEMM) or equivalent can be advantageous. Problem-solving abilities, attention to detail, strong communication, and the ability to work collaboratively in multidisciplinary teams are important soft skills. These qualifications ensure safe, accurate accelerator performance and smooth coordination with healthcare professionals in high-stakes clinical environments.

What are the most commonly searched types of Linear Accelerator Engineer jobs in California?

The most popular types of Linear Accelerator Engineer jobs in California are:

What are popular job titles related to Linear Accelerator Engineer jobs in California?

For Linear Accelerator Engineer jobs in California, the most frequently searched job titles are:

What job categories do people searching Linear Accelerator Engineer jobs in California look for?

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What cities in California are hiring for Linear Accelerator Engineer jobs?

Cities in California with the most Linear Accelerator Engineer job openings:

Linear Accelerator Engineer jobs near you
Infographic showing various Linear Accelerator Engineer job openings in California as of September 2026, with employment types broken down into 1% Internship, 86% Full Time, 10% Part Time, 1% Temporary, and 2% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution, with an average salary of $88,015 per year, or $42.3 per hour.

Senior AI Inference Engineer - Model Optimization & Deployment

San Diego, CA

Zoox
Manufacturing • 1 - 5K employees
Learn more about Zooxexternal

$225K - $305K/yr

Full-time

Medical, Life, PTO

Re-posted 7 days ago

Key responsibilities

  • Optimize large-scale models using quantization, pruning, mixed-precision inference frameworks, and parameter-efficient fine-tuning techniques.

  • Architect and implement model conversion and compilation pipelines using TensorRT for edge deployment.

  • Develop and optimize custom ML operations and TensorRT Plugins with CUDA kernels to ensure real-time, low-latency inference on vehicle systems.

Zoox rating

7.8

Company rating: 7.8 out of 10

Based on 21 frontline employees who took The Breakroom Quiz

Job description

The Perception team is pioneering the development of a multi-modality foundation model to drive the next generation of autonomous system intelligence.

As a Model Optimization & Deployment Engineer, you will focus on bringing highly efficient, production-ready large-scale models to our on-vehicle stack. We are looking for experts with hands-on experience in compressing, accelerating, and deploying complex models (LLMs, VLMs, or FMs) for power- and thermal-constrained vehicle SOCs. You will optimize the ML models, write custom CUDA kernels, and build highly concurrent inference code to ensure real-time, deterministic execution on edge devices.
In this role, you will:
  • Optimize large-scale models (Multi-Modal Sensor Fusion models, LLMs, VLMs) using advanced quantization (PTQ, QAT), pruning, mixed-precision inference frameworks, and parameter-efficient fine-tuning (LoRA, QLoRA).
  • Architect and implement model conversion and compilation pipelines using TensorRT for edge deployment.
  • Perform rigorous parity checking, accuracy recovery, and latency benchmarking between PyTorch frameworks and compiled edge binaries.
  • Develop and optimize custom ML OPs and TensorRT Plugins with efficient CUDA kernels to minimize latency and maximize memory bandwidth on AI accelerators.
  • Write production-level, low latency, and memory-safe C++ and CUDA code for real-time inference on vehicle systems.
Qualifications:
  • Deep expertise in model quantization (PTQ, QAT) and mixed-precision inference frameworks (INT8, FP8, FP4, BF16/FP16).
  • Proven experience optimizing large-scale models (Multi-Modal Sensor Fusion models, LLMs, VLMs/VLAs) utilizing Efficient Attention mechanisms (e.g., FlashAttention, Linear Attention), KV-cache optimization (e.g., PagedAttention) and Speculative Decoding.
  • Extensive experience with model conversion/compilation pipelines (e.g., ONNX, TensorRT, torch.compile) and performing rigorous latency benchmark and model quality parity valuation.
  • Proficiency in low-level programming for AI accelerators, specifically developing and optimizing custom ML OPs and TensorRT Plugins with efficient CUDA kernel implementations.
  • Production-level C++ (14/17/20) and Python programming skills, with experience developing concurrent, memory-safe, real-time inference code for edge devices.
Bonus Qualifications:
  • Familiarity with SOTA autonomous driving perception algorithms (temporal 3D object detection, BEV, 3D Occupancy Networks) and multi-modal sensor processing (Vision, LiDAR, Radar).
  • Experience with distributed training pipelines and model/tensor parallelism (PyTorch Distributed, Ray, DeepSpeed, Megatron-LM) and runtime efficiency optimization for GPU clusters.
  • Experience with end-to-end autonomous driving paradigms (VLM/VLA models, Foundation models) and edge deployment technologies (e.g., TensorRT-LLM).
Base Salary Range
 
There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
 
Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.
About Zoox
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.

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Accommodations
If you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter.

A Final Note:
You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

What Zoox employees say

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Benefits

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About Zoox

Sourced by ZipRecruiter

Zoox is dedicated to creating the world's first fully autonomous vehicle fleet and the necessary ecosystem to make this technology accessible. Positioned at the crossroads of robotics, machine learning, and design, Zoox strives to deliver the future of mobility-as-a-service in urban settings. We seek exceptional individuals who share our enthusiasm and are eager to join a dynamic and results-driven team.

Industry

Manufacturing

Company size

1,001 - 5,000 Employees

Headquarters location

Foster City, CA, US

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