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About This Role
About Sandia
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Sandia National Laboratories is the nation’s premier science and engineering lab for national security and technology innovation, with teams of specialists focused on cutting\-edge work in a broad array of areas. Some of the main reasons we love our jobs:
- Challenging work with amazing impact that contributes to security, peace, and freedom worldwide
- Extraordinary co\-workers
- Some of the best tools, equipment, and research facilities in the world
- Career advancement and enrichment opportunities
- Flexible work arrangements for many positions include 9/80 (work 80 hours every two weeks, with every other Friday off) and 4/10 (work 4 ten\-hour days each week) compressed workweeks, part\-time work, and telecommuting (a mix of onsite work and working from home)
- Generous vacation, strong medical and other benefits, competitive 401k, learning opportunities, relocation assistance and amenities aimed at creating a solid work/life balance\*
World\-changing technologies. Life\-changing careers. Learn more about Sandia at: http://www.sandia.gov
- These benefits vary by job classification.
What Your Job Will Be Like
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Are you a AI cybersecurity researcher who enjoys a challenge? Are you passionate about understanding AI/ML and whether it is securely deployed? Research dedicated to keeping AI systems safe and thwarting cyber intrusions? Are you dreaming of a chance to develop protective technologies, conduct threat assessments, and analyze government, military, and civilian systems that make use of AI? If so, this is the opportunity for you to join Sandia's unique multidisciplinary team committed to solving the Artificial Intelligence security challenges facing our nation. As part of our team, you will engage in work across the technology spectrum including embedded, mobile, desktop, enterprise, and cloud systems, as well as globally connected networks of networks that make use of AI at the edge with a focus on how to protect these technologies from cyber\-attack.
We are looking for experienced AI focused cybersecurity scientists and engineers to join Sandia's national security missions.
On any given day, you may be called on to:
- Developing state of the art approaches for analyzing the security and robustness of AI systems.
- Applying these approaches to understanding vulnerabilities in AI systems and how attackers adapt their tradecraft to exploit those vulnerabilities.
- Reverse engineering malicious code in support of high\-impact customers.
- Low\-level research in multiple levels of firmware.
- Analyzing device software, hardware, and communications protocols.
- Designing and developing new analysis methods and tools.
- Working to identify and address emerging and complex threats to AI systems and effectively participating in the broader security community.
- Influencing the AI security and vulnerability disclosure ecosystems.
- Evaluating the effectiveness of tools, techniques and processes developed by the AI security research government community.
- Uncovering and shaping some of the fundamental assumptions underlying current best practice in AI security.
- Developing thought models, tools and data sets that can be used to characterize the threats to, and vulnerabilities in, AI systems.
- Identifying opportunities to apply AI to improve existing cybersecurity research.
Applicants on this requisition may be interviewed by multiple organizations at Sandia National Laboratories.
Some travel is required.
Due to the nature of the job, the selected candidate must be able to work onsite.
Salary Range
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$117,500 \- $235,700
- Salary range is estimated, and actual salary will be determined after consideration of the selected candidate's experience and qualifications, and application of any approved geographic salary differential.
Qualifications We Require
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- A Bachelor's degree in a relevant discipline and five (5\) years of directly relevant experience, or an equivalent combination of directly relevant education and engineering or scientific experience that demonstrates the knowledge, skills, and ability to perform independent research and development.
- Ability to obtain and maintain a DOE Q and SCI clearance which may require a polygraph test.
Qualifications We Desire
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The ideal R\&D Cybersecurity candidate for Sandia National Laboratories will in addition possess the following:
- Graduate degree in Computer Science/Engineering, Electrical Engineering, Computer Information Systems, Computer Forensics, Mathematics or a directly related field where an independent research project was a graduation requirement (e.g., independent project, thesis, or dissertation).
- Experience in one or more of the following: reverse engineering, software vulnerability assessment, web application assessment, computer networking, computer architecture, compilers, or similar computer security topics.
- Proficiency in scripting or high\-level programming.
- Familiarity with secure\-system design principles and information assurance principles.
- Excellent communication skills and a demonstrated ability to develop technical ideas and results and present them in oral and written form in a concise manner.
Also, for this posting we are seeking individuals with the following experience:
- Five or more years of experience in AI/ML and cybersecurity
- A deep interest in AI/ML and cybersecurity with natural intellectual curiosity and a desire to make broad impact throughout the United Stated Government.
- Experience with adversarial machine learning, ML vulnerabilities, and attack vectors
- Practical experience with applying cybersecurity knowledge toward vulnerability research, analysis, and mitigation.
- You have familiarity with common AI/ML software packages and tools (e.g., Numpy, Pytorch, Tensorflow, etc.)
- Using LLM's and agentic models.
- Implementing and engineering AI systems.
- Knowledge of the AI state of the art and proven desire to keep that knowledge up to date.
- Proficiency in scripting, Python, C/C\+\+, high and/or low\-level programming.
- Familiarity with reverse engineering tools such as Ghidra or IDA Pro.
- Experience developing frameworks, methodologies, or assessments to evaluate effectiveness and robustness of technologies.
- Active DOE Q or DOD TS security clearance with the ability to obtain and maintain an SCI.
- Excellent communication skills and a demonstrated ability to develop technical ideas and results and present them in oral and written form in a concise manner.
About Our Team
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As a Federally Funded Research and Development Center (FFRDC), Sandia National Laboratories is continually asked to help address the country's most pressing national security needs. In the Threat Intelligence Center, the mission of the Information Operations Program is to assess, design, implement and influence the development of national security\-related information systems and technologies in support of Defense and Intelligence customers and their national security missions.
Posting Duration
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This posting will be open for application submissions for a minimum of three (3\) calendar days, including the 'posting date'. Sandia reserves the right to extend the posting date at any time.
Security Clearance
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Sandia is required by DOE to conduct a pre\-employment drug test and background review that includes checks of personal references, credit, law enforcement records, and employment/education verifications. Applicants for employment need to be able to obtain and maintain a DOE Q\-level security clearance and SCI access, both of which require US citizenship. SCI access may also require a polygraph examination. If you hold more than one citizenship (i.e., of the U.S. and another country), your ability to obtain these levels of access may be impacted.
Applicants offered employment with Sandia are subject to a federal background investigation to meet the requirements for access to classified information or matter if the duties of the position require a DOE security clearance. Substance abuse or illegal drug use, falsification of information, criminal activity, serious misconduct or other indicators of untrustworthiness can cause a clearance to be denied or terminated by the DOE, resulting in the inability to perform the duties assigned and subsequent termination of employment.
EEO
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All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status and any other protected class under state or federal law.
NNSA Requirements for MedPEDs
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If you have a Medical Portable Electronic Device (MedPED), such as a pacemaker, defibrillator, drug\-releasing pump, hearing aids, or diagnostic equipment and other equipment for measuring, monitoring, and recording body functions such as heartbeat and brain waves, if employed by Sandia National Laboratories you may be required to comply with NNSA security requirements for MedPEDs.
If you have a MedPED and you are selected for an on\-site interview at Sandia National Laboratories, there may be additional steps necessary to ensure compliance with NNSA security requirements prior to the interview date.
Salary Context
This $117K-$235K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Sandia National Laboratories, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($176K) sits 19% below the category median. Disclosed range: $117K to $235K.
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.
Sandia National Laboratories AI Hiring
Sandia National Laboratories has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Albuquerque, NM, US. Compensation range: $235K - $235K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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