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About This Role
### Company Description
Join us and make YOUR mark on the World!
Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world\-changing impact advancing science and technology to strengthen U.S. security and promote global stability.
Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi\-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.
### Job Description
We have an opening for a Senior AI Architect to lead the design, development, deployment, and operationalization of advanced generative AI applications and services that support mission and operational need within the National Ignition Facility. You will help translate emerging AI technologies into production\-ready capabilities by building backend services, APIs, orchestration layers, and integration patterns that enable AI applications to interact with existing operations, technical, and knowledge\-based data sources. This position is in the National Ignition Facility Computing (NIFC) Division within the Computing Directorate.
*This position offers a hybrid schedule, blending in\-person and virtual presence. You will have the flexibility to work from home one or more days per week.*
This position will be filled at either the SES.3 or SES.4 level based on knowledge and related experience as assessed by the hiring team. Additional job responsibilities (outlined below) will be assigned if hired at the higher level.
You will
- Lead the architecture, design, development, deployment, and lifecycle support of GenAI\-enabled applications and services that address mission and operational needs.
- Design and implement scalable, secure, and maintainable AI solutions across cloud\-based and on\-premises infrastructure, including Kubernetes\-based environments and open\-source or commercially supported AI toolchains.
- Evaluate use cases and determine appropriate technical architectures for retrieval\-grounded GenAI applications, semantic search, LLM\-enabled workflows, RESTful APIs, agentic integration patterns, and related AI services.
- Develop and maintain backend services, APIs, orchestration layers, and application integration components that enable AI applications to securely access, transform, and use operational, technical, and knowledge\-based data sources.
- Drive engineering approaches that improve AI application quality and operational effectiveness, including prompt design, grounding strategies, evaluation methods, observability, performance optimization, cost\-aware implementation, and appropriate safeguards.
- Serve as a technical leader by guiding architectural decisions, sharing best practices, and helping build broader team capability in AI application development and support.
- Partner with customers, domain experts, developers, and infrastructure teams to translate operational needs into production\-ready AI solutions and reusable technical capabilities.
- Perform other duties as assigned.
Additional responsibilities at the SES.4 Level
- Provide technical leadership and strategic direction for departmental GenAI architecture, platform patterns, and integration approaches, ensuring solutions are scalable, secure, reusable, and aligned with long\-term organizational needs.
- Lead complex, cross\-functional AI initiatives; make high\-impact technical decisions; and serve as a senior advisor to management and engineering teams on GenAI architecture, implementation strategy, platform evolution, and technology selection.
- Serve as a mentor and help establish best practices in GenAI engineering, API\-first design, platform portability, and lifecycle management to strengthen team capability and enable successful delivery across multiple products and projects.
### Qualifications
- Ability to obtain and maintain a US DOE Q\-level security clearance which requires U.S. Citizenship.
- Bachelor’s degree in a computer or engineering related field, or the equivalent combination of education and related experience.
- Significant experience architecting, developing, deploying, and supporting complex software systems, including GenAI\-enabled or large language model\-based applications, in production or operational environments.
- Advanced experience designing and implementing secure, scalable solutions across cloud\-based and/or on\-premises environments, such as containerized and/or Kubernetes\-based platforms.
- Significant experience developing backend applications, RESTful APIs, service integrations, and application workflows using languages and frameworks such as Python, Java, JavaScript, or TypeScript.
- Advanced knowledge of GenAI application design patterns and techniques, such as prompt engineering, retrieval and grounding strategies, semantic search, evaluation methods, model integration, and safeguards for reliable and context\-appropriate responses.
- Advanced verbal and written communication skills and a proven ability to lead technical efforts, influence architectural decisions, mentor engineers, and collaborate effectively with customers, developers, infrastructure teams, and management.
Additional qualifications at the SES.4 level
- Highly advanced experience leading complex, multi team efforts.
- Expert experience defining architectural direction and platform patterns across cloud\-based, on\-premises, and containerized environments.
- Highly advanced experience leading design and implementation of reusable services and integration architectures.
- Expert experience providing strategic technical leadership and influencing organizational direction.
Qualifications We Desire
- Master’s degree in a computer or engineering related field with an emphasis on Machine Learning or Artificial Intelligence.
- Experience evaluating and implementing open\-source AI frameworks, model\-serving platforms, semantic retrieval or vector\-based search technologies, and related tooling to support portable AI solutions across cloud and on\-premises environments.
- Experience designing agentic integration patterns, MCP\-enabled services, developer\-facing APIs, or related interfaces that support AI assistants, automation tools, and customer\-developed applications.
- Experience establishing engineering practices for AI systems, including CI/CD, observability, performance tuning, security, and lifecycle management in regulated, mission\-critical, or high\-availability environments.
Pay Range
$175,530 \- $267,060 Annually
$175,530 \- $222,564 Annually for the SES.3 level
$210\.630 \- $267,060 Annually for the SES.4 level
This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs.
### Additional Information
\#LI\-Hybrid
Position Information
This is a Career Indefinite position, open to Lab employees and external candidates.
Why Lawrence Livermore National Laboratory?
- Included in 2026 Best Places to Work by Glassdoor!
- Flexible Benefits Package
- 401(k)
- Relocation Assistance
- Education Reimbursement Program
- Flexible schedules (\*depending on project needs)
- Our values \- visit https://www.llnl.gov/inclusion/our\-values
Security Clearance
This position requires a Department of Energy (DOE) Q\-level clearance. If you are selected, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing. Q\-level clearance requires U.S. citizenship.
Pre\-Employment Drug Test
External applicant(s) selected for this position must pass a post\-offer, pre\-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.
Wireless and Medical Devices
Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.
If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.
How to identify fake job advertisements
Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under “Find Your Job” of our website. If you have encountered a job posting or have been approached with a job offer that you suspect may be fraudulent, we strongly recommend you do not respond.
To learn more about recruitment scams: https://www.llnl.gov/sites/www/files/2023\-05/LLNL\-Job\-Fraud\-Statement\-Updated\-4\.26\.23\.pdf
Equal Employment Opportunity
We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.
Reasonable Accommodation
Our goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory. If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request.
California Privacy Notice
The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job applicants, employees, and non\-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here.
Salary Context
This $175K-$267K range is above the 75th percentile for AI Architect roles in our dataset (median: $171K across 28 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,736 AI roles we're tracking, AI Architect positions make up 1% of the market. At Lawrence Livermore National Laboratory, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $212,500 based on 108 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $175K to $267K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,650. 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: $248,100; VP: $250,000.
Lawrence Livermore National Laboratory AI Hiring
Lawrence Livermore National Laboratory has 2 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Based in Livermore, CA, US. Compensation range: $154K - $267K.
Location Context
Across all AI roles, 15% (562 positions) offer remote work, while 3,158 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 3,736 open positions tracked in our dataset. By seniority: 109 entry-level, 1,755 mid-level, 1,486 senior, and 386 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (562 positions). The remaining 3,158 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,650. 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 hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 3,736 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,564), Data Scientist (311), AI Software Engineer (277). 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 (109) are outnumbered by mid-level (1,755) and senior (1,486) 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 386 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (562 positions), with 3,158 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 $253,650, 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 (1,942 postings), Aws (1,175 postings), Azure (881 postings), Rag (827 postings), Gcp (718 postings), Prompt Engineering (590 postings), Pytorch (586 postings), Claude (528 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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