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About This Role
What You Will Do
This position is open for external candidates only to apply.
Join the High Performance Computing Environments Group (HPC-ENV) at Los Alamos National Laboratory to support the computing infrastructure that enables scientific modeling and simulation, artificial intelligence and machine learning, and emerging computing technologies. As a member of a small team, you will play a direct role in operating production systems, managing HPC scheduling and resource allocation, and improving system reliability and efficiency.
We are a small team, and each member plays a direct role in operating our production systems. We are seeking an experienced professional in HPC scheduling and resource management to lead the team in ensuring the reliable operation of our production systems, which include:
- Large-Scale Modeling & Simulation (e.g., physics, engineering, climate) infrastructure
- Artificial Intelligence & Machine Learning (AI/ML) deployments
- Emerging Architectures for next-generation computing needs
This position will be filled at either the HPC Engineer 2 or HPC Engineer 3 level, depending on the skills of the selected candidate. Additional job responsibilities, outlined below, will be assigned if the candidate is hired at the higher level.
HPC Engineer 2 ($106,400-$176,000)
In this role, you will:
- Configure, deploy, maintain, and troubleshoot HPC job scheduling software and associated databases on existing and new production systems.
- Analyze scheduling performance and optimize resource allocation for modeling and simulation, AI/ML, and other computing workloads.
- Develop and maintain workflow automation, continuous integration and continuous delivery (CI/CD) pipelines, and Infrastructure as Code (IaC) solutions for testing, provisioning, configuration, and deployment.
- Collaborate with vendors and internal technical teams to resolve issues, implement updates, and integrate scheduling solutions with new computing architectures.
- Provide technical direction, coordinate project activities and task execution, and provide input to group management during annual performance reviews.
- Participate in an on-call rotation to support production operations.
HPC Engineer 3 ($128,000-$215,900)
In addition to the duties outlined above, you will:
- Lead the deployment and integration of new HPC systems and architectures, including automated provisioning and configuration.
- Lead efforts to optimize scheduling and resource utilization across large-scale simulation and AI/ML workloads.
- Work with stakeholders and vendors to define system requirements and guide technical solutions.
- Evaluate and introduce new approaches to HPC system management, automation, and orchestration.
- Partner with production system administrators to diagnose and resolve complex system issues affecting applications running on HPC resources.
What You Need
Minimum Job Requirements:
- Linux Administration and DevOps: Demonstrated experience administering Linux systems and applying DevOps practices to support reliable system operations.
- Automation and Programming: Experience automating technical workflows and programming or scripting in languages such as Python, Bash, C, or C++.
- Computing Environments: Working knowledge of one or more of the following: large-scale system administration; job scheduling and modeling and simulation workflows; or AI/ML infrastructure, deployment, and tooling.
- Technical Direction and Collaboration: Ability to provide technical direction, coordinate work, and collaborate with team members, vendors, and internal partners to resolve technical issues and support production operations.
Additional Job Requirements for HPC Engineer 3:
In addition to the requirements outlined above, qualification at the higher level requires:
- Production HPC Operations: Demonstrated experience managing production HPC environments and resolving complex operational issues.
- Technical Leadership: Demonstrated accomplishments serving as a technical lead for a project, including guiding technical work and coordinating contributions from others.
- Scheduling and Resource Management: Demonstrated experience administering an HPC or AI job scheduler or resource manager, such as Slurm, Moab, LSF, PBS/Torque, Grid Engine, Flux, or Run.
- Parallel Runtime Systems: Experience working with the internals of Message Passing Interface (MPI) implementations, such as Open MPI or MPICH; PMIx; or a similar parallel runtime model.
- Automation and Orchestration: Knowledge of automation, orchestration, and deployment tools such as Ansible, Argo CD, Kubernetes, Terraform, or similar technologies.
Education/Experience for the lower level: Positions requires a Bachelor' degree in a STEM field from an accredited college and university and 4 years of related experience, typically with post-doctoral research experience at a university or national lab or equivalent experience directly related to the occupation
Education/Experience for the higher level: Position requires a Master's degree in a STEM field from an accredited college or university and 6 years of relevant experience or an equivalent combination of education and experience directly related to the occupation.
Desired Qualifications:
- Knowledge of virtualization, containerization, and resource orchestration
- Expertise in managing large, complex computing environments
- Experience working with, debugging, and adding features to large code bases in languages like C and C++.
- Experience with monitoring, dashboards, and data visualization (i.e. Splunk, Grafana)
- Experience with OCHAMI or similar HPC cluster management frameworks
- Understanding of cloud platforms and cloud-native DevOps practices (AWS, Azure, GCP)
- Experience with GitLab CI, ArgoCD, or similar continuous delivery tools
Work Location: The work location for this position is hybrid and is located in Los Alamos, NM. Hybrid is defined as working partially onsite/partially offsite but within 2 hours ground commute of this location. All work locations are at the discretion of management and can change at any time with appropriate notice.
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: We encourage applicants to include a cover letter. This should directly address the requirements and qualifications above that you meet, referencing them by number.
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: $176K across 1540 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 9,653 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($161K) sits 24% below the category median. Disclosed range: $106K to $215K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Los Alamos National Laboratory AI Hiring
Los Alamos National Laboratory has 5 open AI roles right now. They're hiring across AI/ML Engineer. Based in Los Alamos, NM, US. Compensation range: $158K - $266K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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