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What You Will Do
This position is for a postdoctoral researcher in the Space Remote Sensing and Data Science Group (ISR\-6\) at Los Alamos National Laboratory. You will work closely with interdisciplinary teams on projects related to remote sensing, data science, image and signal processing, machine learning, and sensor data analysis. Responsibilities will be tailored to your background and interests, with opportunities to contribute to innovative research and development.
As a postdoctoral researcher, you will be involved in advancing methods and technologies for collecting, processing, analyzing, and interpreting data from space, airborne, ground\-based, and laboratory remote\-sensing systems. Candidates should have broad technical skills and an interest in applying computational and analytical techniques to scientific challenges.
In this role, you may contribute to one or more of the following areas:
- Developing and applying algorithms to process and analyze sensor data (including images, spectra, time series, or other types)
- Using statistical, mathematical, or computational methods to address detection and characterization problems
- Modeling sources, sensors, and measurement uncertainties
- Analyzing and interpreting experimental or field\-collected data
- Contributing to the design or calibration of sensing systems and instruments
- Participating in laboratory, field, or deployment activities to collect and evaluate remote\-sensing data
- Communicating research findings through reports, presentations, publications, and teamwork
What You Need
Minimum Job Requirements:
- PhD in engineering, physical sciences, computer science, mathematics, or a related field (or will have completed by start date)
- General experience in scientific computing, data analysis, or algorithm development
- Ability to work collaboratively with diverse teams and communicate research results effectively
- Demonstrated strong analytical problem\-solving abilities to overcome technical challenges in remote sensing and data science challenges
Desired Qualifications:
- Familiarity with one or more areas such as image/signal processing, machine learning, statistical analysis, sensor systems, or scientific software
- Experience with programming languages or scientific computing tools
- Experience with experimental or field research, instrumentation, or data collection
- Willingness to develop new skills and contribute to collaborative research projects
Physical Requirements:
- Physical movement between buildings located primarily within a half\-mile radius, which may include movement across uneven and/or inclined ground
- Physical movement within large interior facility spaces and corridors, which may require travel up and down stairs
- Direct interaction with hardware, equipment, and instruments in office and laboratory environments
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.
Note to Applicants:
Please submit a resume and a cover letter describing your research interests, relevant experience, and how your background aligns with the position. Highlight any experience in scientific computing, data analysis, remote sensing, sensor research, or other areas relevant to the role.
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)
ISR\-6 is a research group that designs and deploys advanced sensing systems and analysis tools for mission areas including nuclear non\-proliferation, space domain awareness, atmospheric and planetary science, and applied intelligence. The group's staff includes engineers, scientists, and data specialists working collaboratively to develop cutting\-edge technologies.
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.
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 applyhelp@lanl.gov or call (505\)\-664\-6947\.
Instructions on How to Activate/Create a LANL Jobs Account:
Follow the instructions below if you have ever had an employee Z number, been a contractor, or received Los Alamos Lab insurance coverage to activate your account:
- Select the Click Here button if you have been employed with the Lab or received insurance coverage.
- Please enter only your first and last name and current email address (an email with your validation code will be sent to you) to activate the account currently in our system.
- Enter your validation code as described in the email you receive and complete the 3\-page registration form. Your account is now active, and you can apply for jobs or save to your basket. Important: Enter the validation code within 15 days to activate your account or your account will be deactivated.
Follow the instructions below if you if you have never been employed with the Lab or received insurance coverage to create an account:
- Select the Register button if you have never been employed with the Lab or received insurance coverage to Create an Account.
- From here, you will establish an account with username and password.
How to apply:
Login to Your Account to Complete the Application Process
- Click the Vacancy Name number (in blue) to view any job's details.
- Click Apply or Add to Basket to apply later. Tip: To apply for a job or save your basket, you must have a LANL jobs account.
If you experience any technical issues, please email applyhelp@lanl.gov for assistance.
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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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
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.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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