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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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We are seeking a motivated AI engineer to join Department 5328, Smart Electronics Software, in advancing the way high\-TRL software is developed and deployed on mission\-relevant electronics platforms. In this role, you will help pioneer the use of agentic coding workflows to accelerate the implementation, verification, and sustainment of robust software for embedded processors, real\-time systems, and FPGA\-based architectures.
You will work as part of a multidisciplinary team focused on bringing artificial intelligence and autonomy closer to the edge. This includes deploying AI algorithms onto constrained electronics platforms to enable real\-time autonomy, decision support, signal processing, and intelligent control in operational environments. The work will span concept development, rapid prototyping, hardware/software integration, testing, and transition to high\-reliability applications.
On any given day, you may be called on to:
- Develop, test, and integrate high\-TRL software for embedded, electronics, and FPGA platforms.
- Apply agentic coding tools and AI\-assisted development methods to improve software productivity, quality, and maintainability.
- Implement AI, machine learning, and autonomy algorithms for real\-time execution on resource\-constrained hardware.
- Collaborate with electronics, firmware, software, systems, and mission\-domain experts to develop integrated solutions.
- Optimize algorithms and software for performance, latency, power, memory, and reliability constraints.
- Support hardware\-in\-the\-loop testing, embedded platform bring\-up, verification, validation, and fieldable prototype demonstrations.
- Contribute to software architectures, development workflows, and best practices for trusted AI\-enabled embedded systems.
- Explore emerging tools and methodologies for applying generative AI and autonomous coding agents to mission software development.
This position is ideal for someone who enjoys working at the intersection of modern software engineering, embedded systems, FPGA development, artificial intelligence, and national security applications. You will have the opportunity to shape how next\-generation intelligent systems are developed and deployed on advanced electronics platforms.
Due to the nature of the work, the selected applicant will be required to work onsite. Relocation provided for those who qualify.
Applicants on this requisition may be interviewed by multiple organizations at Sandia National Laboratories.
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 acquire and maintain a DOE Q clearance and SCI clearance, which may require a polygraph.
Qualifications We Desire
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The ideal R\&D S\&E AI candidate for Sandia National Laboratories will in addition possess the following:
- Graduate degree in a relevant computationally\-intensive discipline where an independent research project was a graduation requirement (e.g., independent project, thesis, or dissertation).
- Experience in developing software and AI systems for enterprise and national security applications.
- Demonstrated software development skills and familiarity with modern software development practices.
- Proven ability to work and communicate effectively in a collaborative and interdisciplinary team environment.
Also for this posting we are seeking individuals with the following experience:
- Demonstrated experience designing, developing, integrating, and deploying high\-reliability software on embedded, electronics, real\-time, or FPGA\-based platforms.
- Proven ability to lead software or firmware development efforts from requirements definition through architecture, implementation, integration, verification, validation, and delivery.
- Experience developing or deploying AI, machine learning, signal processing, autonomy, or decision\-support algorithms on edge, embedded, GPU, FPGA, SoC, or other resource\-constrained hardware platforms.
- Strong proficiency in one or more relevant programming languages, such as C, C\+\+, Python, Rust, VHDL, Verilog, SystemVerilog, MATLAB, or similar languages used in embedded, FPGA, or high\-performance systems.
- Experience with embedded Linux, real\-time operating systems, bare\-metal development, hardware abstraction layers, device drivers, communication interfaces, or board support packages.
- Experience with FPGA and digital design workflows, including RTL development, high\-level synthesis, timing closure, simulation, synthesis, hardware/software co\-design, and FPGA\-based acceleration.
- Demonstrated ability to optimize software or algorithms for latency, throughput, power, memory, determinism, reliability, or hardware resource constraints.
- Experience establishing or improving software engineering practices for high\-TRL systems, including requirements traceability, configuration management, automated testing, code quality, cybersecurity considerations, documentation, release processes, and maintainability.
- Familiarity with verification and validation approaches for real\-time, embedded, AI\-enabled, or mission\-critical systems, including hardware\-in\-the\-loop testing, simulation environments, test automation, and performance characterization.
- Experience applying AI\-assisted development, agentic coding workflows, automated code generation, or advanced developer tooling to improve productivity, quality, testing, documentation, or sustainment of software systems.
- Ability to evaluate emerging AI coding tools, define appropriate guardrails, and integrate them into disciplined engineering workflows for high\-consequence or high\-reliability software.
- Demonstrated technical leadership in multidisciplinary teams, including coordinating work across software, firmware, electronics, systems engineering, AI/ML, test, and mission\-domain contributors.
- Experience mentoring junior staff, interns, technologists, or students in software engineering, embedded development, FPGA workflows, AI deployment, or modern development practices.
- Ability to interface with customers, sponsors, and stakeholders to understand mission needs, define technical approaches, communicate risks, and deliver impactful solutions.
- Experience transitioning prototypes, research software, or laboratory demonstrations into robust, maintainable, deployable systems.
- Strong written and verbal communication skills, including experience preparing technical reports, design documentation, test plans, peer\-reviewed publications, proposals, or briefings.
About Our Team
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Smart Electronics Software specializes in next\-level software development, delivering custom\-built solutions tailored to address the unique challenges of complex national security problems. Committed to software best practices, our team harnesses our diverse expertise, modern tools, and advanced methodologies to create robust, reliable, and cutting\-edge applications that drive innovation and operational excellence.
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 above the median 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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($176K) sits 18% below the category median. Disclosed range: $117K to $235K.
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.
Sandia National Laboratories AI Hiring
Sandia National Laboratories has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Albuquerque, NM, US. Compensation range: $199K - $235K.
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
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