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About This Role
Lawrence Berkeley National Laboratory is hiring an AI Research Software Engineer. This position is within the Advanced Light Source (ALS) Accelerator Physics Group (APG), part of the Accelerator Technology \& Applied Physics (ATAP) Division. The APG is responsible for ensuring the operation, performance, and continuous improvement of the ALS accelerator complex.
The role advances artificial intelligence and machine learning for accelerator operations, spanning both applied research and the software that puts it into practice. The objective is to develop AI/ML methods, and the software that supports them, that make the ALS accelerator accessible to modern AI systems — enabling real\-time machine optimization, AI\-assisted operations, and intelligent automation of accelerator tuning and diagnostics. The work combines investigation of new approaches with their deployment in an operational environment.
The person in this position will connect accelerator control systems with modern AI/ML capabilities, supporting both existing optimization tools and next\-generation autonomous agent systems for accelerator operations. The role is especially timely with the upcoming ALS Upgrade (ALS\-U) commissioning, which offers the opportunity to establish these methods and tools correctly from the start.
You will:
- Conduct applied research in artificial intelligence and machine learning methods for accelerator optimization, diagnostics, and operations.
- Develop and deploy machine learning models and autonomous AI agent systems for accelerator optimization, diagnostics, and operations.
- Design and develop the software and interfaces that connect accelerator control systems with AI/ML applications.
- Contribute to the group’s software and computing environment, including control\-system integration and operational tools.
- Collaborate with accelerator physicists, controls engineers, and operations staff to identify opportunities for automation and to translate physics requirements into working solutions.
- Disseminate results through publications in peer\-reviewed journals and presentations at relevant conferences and workshops.
- Apply sound development practices to produce reliable, well\-documented, and maintainable software.
- Support the operation of the ALS accelerator systems, including availability during off\-hours as needed.
- Participate in the commissioning activities of the ALS Upgrade as they relate to AI/ML systems.
- Comply with LBNL/ALS Environment, Safety, and Health (EH\&S) practices and requirements.
We are looking for:
- Master’s degree in computer science, physics, engineering, or a related field with at least 3 years of related experience; or a Bachelor’s degree with at least 5 years; or an equivalent combination of education and experience. A Ph.D. in a relevant field is preferred and counts toward the experience requirement.
- Demonstrated experience developing and deploying software systems in a scientific or engineering environment.
- Strong programming skills, including proficiency in Python and familiarity with modern development practices (version control, testing, documentation).
- Working knowledge of machine learning methods and their application to scientific or engineering problems.
- Demonstrated ability to design and build complex software systems.
- Ability to work effectively in a multidisciplinary team alongside physicists, engineers, and operations staff.
- Good command of written and oral English and communication skills.
- Ability to foster constructive, respectful, and cooperative workplace relationships.
Desired skills/knowledge:
- Experience working at an accelerator facility or similar large\-scale scientific instrumentation environment.
- Experience with accelerator control systems (e.g., EPICS) and real\-time data acquisition.
- Experience with machine learning frameworks (e.g., PyTorch, TensorFlow) and scientific computing libraries (e.g., NumPy, pandas).
- Familiarity with accelerator control system frameworks (e.g., EPICS, Bluesky, ophyd) and real\-time data systems.
- Experience with LLM\-based agent architectures, tool\-use patterns, and autonomous AI systems.
- Experience with containerization (Docker), CI/CD pipelines, and production deployment practices.
- Experience with distributed systems, API design, and real\-time data streaming.
Additional information:
- Application date: Priority consideration will be given to candidates who apply by July 2, 2026. Applications will be accepted until the job posting is removed.
- Appointment type: This is a full\-time career appointment, non\-exempt (hourly paid), eligible for overtime pay.
- Salary range: The full salary range for this position is $9,761/month to $16,473/month and is expected to pay between the targeted range of $9,761 to $13,422/month. Salary for this position will be commensurate with the final candidate’s qualification and experience, including skills, knowledge, relevant education, certifications, and aligned with the internal peer group. It is not typical for an individual to be offered a salary at or near the top of the range for a position.
- Background check: This position is subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.
- Work modality: This position is eligible for a hybrid work schedule \- a combination of teleworking and performing work on site at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA. Work schedules are dependent on business needs. Individuals working a hybrid schedule must reside within 150 miles of Berkeley Lab.
Join our team and YOU will play a supporting role in our goal to address global challenges! Have a high level of impact and work for an organization associated with 17 Nobel Prizes!
*Why join Berkeley Lab?*
We invest in our employees by offering a total rewards package you can count on:
- Exceptional health and retirement benefits, including pension or 401K\-style plans
- Opportunities to grow in your career \- check out our Tuition Assistance Program
- A culture where you’ll belong \- we are invested in our teams!
- In addition to accruing vacation and sick time, we also have a Winter Holiday Shutdown every year.
- Parental bonding leave (for both mothers and fathers)
- Pet insurance
Want to learn more about working at Berkeley Lab? Please visit: careers.lbl.gov
Equal Employment Opportunity Employer: The foundation of Berkeley Lab is our Stewardship Values: Team Science, Service, Trust, Innovation, and Respect; and we strive to build community with these shared values and commitments. Berkeley Lab is an Equal Opportunity Employer. We heartily welcome applications from all who could contribute to the Lab's mission of leading scientific discovery, excellence, and professionalism. In support of our rich global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories under State and Federal law.
Misconduct Disclosure Requirement: As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct, are currently being investigated for misconduct, left a position during an investigation for alleged misconduct, or have filed an appeal with a previous employer.
Salary Context
This $117K-$197K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $190K across 219 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Lawrence Berkeley National Laboratory, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 797 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($157K) sits 32% below the category median. Disclosed range: $117K to $197K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Lawrence Berkeley National Laboratory AI Hiring
Lawrence Berkeley National Laboratory has 1 open AI role right now. They're hiring across AI Software Engineer. Based in San Francisco Bay Area, CA, US. Compensation range: $197K - $197K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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