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About This Role
What You Will Do
The High Performance Computing (HPC) Division at Los Alamos National Laboratory provides scientific computing resources consisting of some of the largest HPC systems in the world as well as numerous large commodity cluster systems and a quickly growing AI/ML/LLM infrastructure. Our Cybersecurity Team within the HPC Systems Group (HPC-SYS) is seeking a highly qualified Cybersecurity AI Specialist member capable of integrating emerging AI cybersecurity frameworks and solutions into our Linux environment. The selected candidate will stay up to date on AI tools and techniques in order to deploy these resources securely within the HPC Division.
HPC Engineer 2: ($106,400- $176,000)
The primary responsibilities will include working closely with HPC Cybersecurity leads to assist implementing HPC's overall AI cybersecurity strategy. The selected candidate will assist in the design and implementation of security controls for AI-specific assets. Develop defenses against AI threats including data poisoning, prompt injection, model extraction, adversarial examples, jailbreaks, and misuse of LLM agents. Evaluate, integrate, and automate modern AI security tools and policies across HPC Division systems, aligning with Los Alamos National Laboratory cybersecurity standards. The selected candidate will work both independently and collaboratively with other members of HPC to provide technical assistance to develop and deploy secure AI resource.
HPC Engineer 3: ($128,000 - $215,900)
In addition to what was outlined at the lower level, at this level the selected candidate will lead the design of HPC's AI cybersecurity strategy. Lead development and deployment of novel solutions to secure AI within an HPC environment. Develop and present technical reports and briefings on system vulnerabilities to convey risks, impacts and recommendations. The selected candidate will represent LANL HPC in collaborations with other divisions and other NNSA/DOE HPC centers. Mentor junior staff and students, and produce technical artifacts (presentations, papers, reports) that advance AI cybersecurity within HPC.
This position will be filled at either the HPC Engineer 2/3 level, depending on the skills of the selected candidate.
What You Need
Minimum Job Requirements:
- Knowledge in one or more of the following disciplines: Cybersecurity, Information Security, Information Assurance, Computer Science. Understanding of classic and contemporary cyber threats and vulnerabilities and demonstrated ability to apply that knowledge to system engineering, analytics, or operations.
- Experience deploying AI resources in a production computing environments
- Understanding of function and role of AI gateways, Retrieval-Augmented Generation (RAG) databases, and Model Context Protocol (MCP) servers
- Working knowledge of agentic workflows, including security risks and operational limitations.
- Working knowledge of AI harnesses used to configure, run, evaluate, and manage AI models and agents.
- Experience with commodity cybersecurity tools
- Experience working in a production computing environment, preferably with HPC systems or at large scale.
- Experience with writing test plans, findings reports or published papers. Strong interpersonal communication skills with the ability to work with groups of people of various levels of technical knowledge or understanding. Demonstrated experience working effectively under the pressure of frequent interruptions and conflicting priorities.
- Demonstrated ability to write and modify scripts in Bash, Perl, Python, or a similar language to automate technical tasks.
- Ability to explain how memory-safe programming languages reduce common software security vulnerabilities.
- Ability to communicate technical information clearly in writing and verbally.
Additional Job Requirements for HPC Engineer 3:
In addition to the requirements outlined above, qualification at the higher level requires:
- Expert knowledge and practical experience at the advanced level in one or more of the following disciplines: Cybersecurity, Information Security, Information Assurance, Computer Science. Expert technical knowledge of classic and contemporary cyber threats and vulnerabilities and demonstrated ability to apply that knowledge to system engineering, analytics, or operations.
- Experience leading the deployment of AI resources in a production computing environments.
- Expert knowledge of AI systems and component pieces such as AI gateways, RAG databases, MCP servers.
- Demonstrated experience and expertise in creating or modifying cybersecurity tools for a production computing environment.
- Experience making significant security contributions to the planning, design, testing and implementation phases for deploying new technologies.
- Expertise in using currently available AI tools
- Excellent written and oral communication skills.
Education/Experience at HPC Engineer 2:
Position Requires a bachelor's in Computer Science, Computer Engineering, or a related field, and 3 years of relevant experience in high performance computing or scalable AI computing, or data center environments or equivalent combination of education and experience in related field.
Education/Experience at HPC Engineer 3:
Position Requires a bachelor's in Computer Science or Computer Engineering or a related field and 6 years of relevant experience in high performance computing or scalable AI computing, or data center environments or equivalent combination of education and experience in related field.
Desired Qualifications:
- Strong Linux knowledge and expertise as an administrator. Broad knowledge of administration of production Linux computer systems, utilities, and tools, including experience building, configuring, and administering production Linux computer systems.
- Experience hardening Linux servers operating in a multi-user environment
- Knowledge of High Performance Computing, including environments and supporting infrastructure. Knowledge of distributed systems, including system architectures, computer networks, software and multi-tenancy.
- Knowledge of integrating MCP servers with legacy systems
- Experience maintaining user provenance within AI workflows
- Experience with automating security processes such as automating alert analysis and integrating multiple security tools together so data from one can feed another.
- Experience using one or more enterprise monitoring tools (such as Splunk, Grafana). Knowledge of and experience with securing web-based user interfaces.
- Possess high attention to detail including excellent organizational skills, analytical thinking, observational skills and problem-solving skills. Ability to multi-task and adjust to the workings of a dynamic and fast paced environment.
- Active DOE "Q" clearance
Work Location: This position will be located in Los Alamos, NM, with the potential for a hybrid work arrangement (60% onsite/40% offsite) from a location within 2 hours ground commute of this location. Reporting onsite will be required. Hybrid is at the discretion of management and can change at any time with appropriate notice.
Position commitment: This position will be located in Los Alamos, NM, with the potential for a hybrid work arrangement (60% onsite/40% offsite) from a location within 2 hours ground commute of this location. Reporting onsite will be required. Hybrid is at the discretion of management and can change at any time with appropriate notice.
Note to Applicants: For consideration, applicants should submit a cover letter addressing how their knowledge, skills and abilities meet the minimum requirements along with a resume.
Due to federal restrictions contained in the current National Defense Authorization Act, citizens of the People's Republic of China-including the special administrative regions of Hong Kong and Macau-as well as citizens of the Islamic Republic of Iran, the Democratic People's Republic of Korea (North Korea), and the Russian Federation, who are not Lawful Permanent Residents ("green card" holders) are prohibited from accessing facilities that support the mission, functions, and operations of national security laboratories and nuclear weapons production facilities, which includes Los Alamos National Laboratory.
Where You Will Work
Located in beautiful northern New Mexico, Los Alamos National Laboratory (LANL) is a multidisciplinary research institution engaged in strategic science on behalf of national security. Our generous benefits package includes:
§ PPO or High Deductible medical insurance with the same large nationwide network
§ Dental and vision insurance
§ Free basic life and disability insurance
§ Paid childbirth and parental leave
§ Award-winning 401(k) (6% matching plus 3.5% annually)
§ Learning opportunities and tuition assistance
§ Flexible schedules and time off (PTO and holidays)
§ Onsite gyms and wellness programs
§ Extensive relocation packages (outside a 50 mile radius)
Additional Details
Directive 206.2 - Employment with Triad requires a favorable decision by NNSA indicating employee is suitable under NNSA Supplemental Directive 206.2. Please note that this requirement applies only to citizens of the United States. Foreign nationals are subject to a similar requirement under DOE Order 142.3A.
Clearance: Q (Position will be cleared to this level). Selected applicants will be subject to a background investigation conducted by or on behalf of the Federal Government, and must meet eligibility requirements* for access to classified matter. This position requires a Q clearance, and obtaining such clearance requires US Citizenship except in extremely rare circumstances. Dependent upon the position, additional authorization to access classified information may be required, which may or may not be available to dual citizens. Receipt of a Q clearance and additional access authorization ultimately is a decision of the Federal Government and not of Triad.
- Eligibility requirements: To obtain a clearance, an individual must be at least 18 years of age; U.S. citizenship is required except in very limited circumstances. See DOE Order 472.2 for additional information.
New-Employment Drug Test: The Laboratory requires successful applicants to complete a new-employment drug test and maintains a substance abuse policy that includes random drug testing. Although New Mexico and other states have legalized the use of marijuana, use and possession of marijuana remain illegal under federal law. A positive drug test for marijuana will result in termination of employment, even if the use was pre-offer.
Regular position: Term status Laboratory employees applying for regular-status positions are converted to regular status.
Internal Applicants: Regular appointment employees who have served the required period of continuous service in their current position are eligible to apply for posted jobs throughout the Laboratory. If an employee has not served the required period of continuous service, they may only apply for Laboratory jobs with the documented approval of their Division Leader. Please refer to Policy Policy P701 for applicant eligibility requirements.
Equal Opportunity: Los Alamos National Laboratory is an equal opportunity employer. All employment practices are based on qualification and merit, without regard to protected categories such as race, color, national origin, ancestry, religion, age, sex, gender identity, sexual orientation, marital status or spousal affiliation, physical or mental disability, medical conditions, pregnancy, status as a protected veteran, genetic information, or citizenship within the limits imposed by federal, state, and local laws and regulations. The Laboratory is also committed to making our workplace accessible to individuals with disabilities and will provide reasonable accommodations, upon request, for individuals to participate in the application and hiring process. To request such an accommodation, please send an email to [email protected] or call (505)-664-6947. Apply
Salary Context
This $106K-$215K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Los Alamos National Laboratory, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($161K) sits 24% below the category median. Disclosed range: $106K to $215K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Los Alamos National Laboratory AI Hiring
Los Alamos National Laboratory has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Los Alamos, NM, US. Compensation range: $215K - $215K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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