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About This Role
SABG is seeking an AI/ML Engineer in Multiple Levels- Level II, III, or IV to support the National Reconnaissance Office (NRO).
JOB TITLE: AI/ML Engineer (Level II, III, or IV)
CLEARANCE: TS/SCI; should have current or be able to pass CI polygraph test
LOCATION:Chantilly, VA
ESSENTIAL DUTIES AND RESPONSIBILITIES: Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of this position.
- Collaborate with modeling and simulation team to conduct a wide variety of analyses in support of government satellite programs in Earth orbits, as well as satellite designers and operations planners
- Develop new Machine Learning (ML) algorithms for applications such as + Using optical and radar space-surveillance sensors to recognize different types of satellites + Scheduling space surveillance sensor networks to find, recognize and track maneuvering satellites + Deciding what activities satellites should undertake
- Algorithm development will include steps such as + Conducting Exploratory Data Analysis (EDA) to identify patterns + Designing, developing, and evaluating machine learning algorithms, including supervised, unsupervised, and reinforcement learning + Building, training, and tuning predictive models + Collaborating with cross-functional teams to identify and solve problems + Implementing and optimizing algorithms for tasks such as anomaly detection, prediction, classification, optimization, clustering + Developing High Performance Computing pipelines for scalability and monitoring
- Use programming languages such as Python, C++, or others to leverage existing analysis tools and develop new ones and work with experts in astrodynamics, sensors, AI/ML, High Performance Computing (HPC)
- Review and validate algorithms and results provided by external teams
- Develop and optionally present actionable technical briefings as well as Conduct independent trade studies and technical assessments
- Mentoring by experts in AI/ML, HPC, astrodynamics, sensing and other fields.
SUPERVISION: This position does not have supervisory responsibilities.
EDUCATION AND EXPERIENCE:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Applied Math, or a related technical field.
- 3+ years of experience in modeling and simulation for aerospace or space systems.
- Active Top Secret/SCI w/Poly Clearance
- Strong understanding of sensor phenomenology—such as optical, infrared, or radar systems—and associated modeling methods.
- Intermediate Python programming experience, demonstrated through hands-on experience with tasks such as data manipulation, automation, and development of Python-based solutions. Experience with libraries such as NumPy, SciPy, pandas, and matplotlib is beneficial.
- Ability to communicate technical results clearly in both written and verbal formats.
COMPUTER SKILLS:
- Skills in operating a personal computer and standard office equipment
- Must have proficiency in a variety of computer software applications in word processing, spreadsheets, and database (MS Office: Word, Excel, PowerPoint, Outlook)
COMPETENCIES: To perform the job successfully, an individual should demonstrate the following competencies:
- Problem Solving - Identifies and resolves problems promptly; gathers and analyzes information skillfully; develops alternative solutions; works well in group problem-solving situations; and uses reason even when dealing with emotional topics.
- Written Communication - Writes clearly and informatively; edits work for spelling and grammar; varies writing style to meet needs; presents numerical data effectively; and able to read and interpret written information.
- Diversity – Shows respect and sensitivity for cultural differences and promotes a harassment-free environment.
- Ethics - Treats people with respect; keeps commitments; Inspires the trust of others; works with integrity and ethically; and upholds organizational values.
- Planning/Organizing - Prioritize and plans work activities; uses time efficiently; plans for additional resources; sets goals and objectives; organizes or schedules other people and their tasks if needed; and develops realistic action plans.
- Professionalism - Tactfully approaches others; reacts well under pressure; treats others with respect and consideration regardless of their status or position; accepts responsibility for own actions; follows through on commitments.
LANGUAGE SKILLS: Ability to read, analyze, and interpret governmental regulations. Ability to write reports, business correspondence, and procedure manuals. Ability to effectively present information and respond to questions from groups of managers, clients, customers, and the public.
MATHEMATICAL SKILLS: Ability to apply concepts such as fractions, percentages, ratios, and proportions to practical situations. Solid understanding of basic mathematical concepts and the ability to apply them in proposal development.
REASONING ABILITY: Ability to define problems, collect data, establish facts, and draw valid conclusions. Ability to question activities and issues in all functional areas and make sound business decisions based on that data.
PHYSICAL DEMANDS: The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations will be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is regularly required to sit, talk, and hear. The employee is regularly required to write and type on a computer. The employee is frequently required to walk; use hands to finger, handle, or feel and reach with hands and arms. The employee is occasionally required to stand; climb, or balance and stoop, kneel, crouch, or crawl. Specific vision abilities required by this job include close vision.
WORK ENVIRONMENT: The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. Job duties are performed in a manufacturing/office environment utilizing standard office equipment such as a computer, photocopier, and telephone. The noise level in the work environment is usually low to moderate.
FOR OUR SERVICE MEMBERS AND VETERANS:SABG values the service Veterans and their family members have given to our country and we support the hiring of returning Service Members and military spouses. If you are a Veteran or wounded warrior and would like assistance with the employment process at SABG, contact our Recruiting team at [email protected].
FOR PERSONS WITH DISABILITIES: If you are a person with a disability or a disabled Veteran and are applying for a job with SABG, we would like to ensure your application process goes as smoothly as possible. If because of a medical condition or disability, you need a reasonable accommodation for any part of the employment process, contact our Recruiting team at [email protected] and let us know the nature of your request and your contact information.
TO APPLY FOR THIS POSITION: Once you complete all the steps, we are notified of your completed application, and we will review. For individuals who meet the requirements of the position, we will contact you and arrange next steps in the interview process.
Job Link: https://secure.entertimeonline.com/ta/SABGLLC.careers?ShowJob=889361296
SALARY: $160,000k - $220,000k
Compensation at SABG is determined based on factors such as the role's requirements, education, certifications, skills, experience, geographic location, and market conditions. Final compensation is based on the specific qualifications and circumstances of each candidate.
SABG is an Equal Opportunity/Affirmative Action Employer
Minorities/Women/Veterans/Disabled
Salary Context
This $160K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At SABG, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($190K) sits 10% below the category median. Disclosed range: $160K to $220K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
SABG AI Hiring
SABG has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chantilly, VA, US. Compensation range: $220K - $220K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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