Interested in this AI/ML Engineer role at Los Alamos National Laboratory?
Apply Now →About This Role
What You Will Do
Join the best and brightest minds in the world at one of the most innovative and creative multidisciplinary research institutions engaged in strategic science on behalf of national security; the work we do at Los Alamos National Laboratory (LANL) matters to our country and the world.
You will work alongside scientific leaders to manage and ensure the timely completion of scientific projects in artificial intelligence and machine learning (AI/ML). This will include working closely with principal investigators and other technical leaders to develop work packages defining milestones and deliverables to meet specific programmatic needs, identifying technical requirements, defining and/or executing budgets and schedules, organizing hiring and onboarding, planning and facilitating visits from a variety of stakeholders, and oversight of the project team in the planning, tracking, and execution of the project from instantiation to completion. You will work with sponsors (both internal and external), customers, line management, program management, and other stakeholders to communicate status updates, challenges, and needs of projects and personnel.
This position is expected to work closely with A\-9\-an AI\-focused team\-of\-teams composed from the various groups within the Analytics, Intelligence \& Technology Division\-by supporting the organization and its constituent teams. A\-9's mission is to advance capabilities related to the use, evaluation, and development of AI as it relates to assessing, preparing for, and responding to potentially catastrophic nuclear, chemical, biological, cyber, natural and emerging technological threats. A\-9 participates in foundational and applied R\&D, threat analysis, communication and education, hardware and software infrastructure, and cross\-organization AI Leadership.
Agility and adaptability are highly prized and emphasized; because the global threat landscape constantly evolves, so too does our R\&D portfolio. Some examples of current stakeholders include the U.S. Department of Energy, National Nuclear Security Administration, Department of War, Department of Homeland Security, Intelligence Community, Defense Threat Reduction Agency, and many others. To achieve this, we maintain a highly multidisciplinary team of subject matter experts, from mathematicians and economists to engineers and physicists, to computer and data scientists, to intelligence analysts.
What You Need
Minimum Job Requirements:
- Experience developing project plans and detailed work breakdown structures, negotiating work packages, and assigning various aspects of work to successfully complete project deliverables.
- Oversees small project team(s) in the planning, tracking, and execution of project plans from initiation to completion.
- Uses judgment within defined practices in identifying potential project risks, developing risk mitigation strategies and corrective actions, and recommending risk acceptance/avoidance for all aspects of the project(s).
- Under direction, serves as the primary spokesperson to report project status and issues to the program manager and to Laboratory senior managers and workers.
- Ability to develop and nurture effective internal customer relationships.
- Working with a multidisciplinary team, have the ability to successfully plan, organize, lead, and monitor team efforts to completion.
- Demonstrated ability to employ strict discretion in sensitive situations.
- Excellent oral and written communication skills and demonstrated ability to interact effectively with all levels of laboratory and external personnel.
- Ability and desire to obtain both Q and SCI clearances, which generally requires U.S. citizenship.
Education/Experience:
Position requires a bachelor's degree in a science or engineering related field, from an accredited institution and 2 years directly related experience; or an equivalent combination of education and experience directly related to the occupation.
Desired Qualifications:
- Previous technical exposure or experience in artificial intelligence, machine learning, large language models, etc.
- Knowledge of relevant funding organizations (e.g., DoW, DHS, DOE, Intelligence Community, etc.) and experience in organizing or facilitating proposals for these organizations.
- Familiarization with common project management software and practices for tracking project and financial data.
- Working knowledge of U.S. priorities with respect to national and global security.
- Active Q and SCI clearances.
Work Environment:
Work Location: The work location for this position is onsite and located in Los Alamos, NM. All work locations are at the discretion of management.
Position commitment: Regular appointment employees are required to serve a period of continuous service in their current position in order to be eligible to apply for posted jobs throughout the Laboratory. If an employee has not served the time required, they may only apply for Laboratory jobs with the documented approval of their Division Leader. The position commitment for this position is 1 year.
Note to Applicants:
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/SCI (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.
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 $87K-$143K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($115K) sits 46% below the category median. Disclosed range: $87K to $143K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Los Alamos National Laboratory AI Hiring
Los Alamos National Laboratory has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Alamos, NM, US. Compensation range: $143K - $143K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.