Interested in this AI/ML Engineer role at The Aerospace Corporation?
Apply Now →About This Role
The Aerospace Corporation is the trusted partner to the nation’s space programs, solving the hardest problems and providing unmatched technical expertise. As the operator of a federally funded research and development center (FFRDC), we are broadly engaged across all aspects of space— delivering innovative solutions that span satellite, launch, ground, and cyber systems for defense, civil and commercial customers. When you join our team, you’ll be part of a special collection of problem solvers, thought leaders, and innovators. Join us and take your place in space.
The Defense Systems Group (DSG) provides analysis\-based decision support to senior leaders on space architectures, policy and strategy, technology development, warfighter capability enablers, systems integration, defense industrial base, and threat reduction to help shape existing and future space missions across the national security space (NSS) enterprise. DSG is unique at Aerospace as it not only directly supports major customers, including the U.S. Space Force, U.S. Space Command, Air Force Material Command, and senior leadership at the Pentagon, but also provides a broad range of technical support across the national space enterprise, maintaining vertical responsibilities with customers’ portfolios as well as horizontal, matrixed responsibilities across the corporation. Additionally, DSG provides deep technical expertise to the Space Force Space Systems Command (SSC) in the conception, design, acquisition, launch and operations of satellite, launch vehicle, ground control, and range systems.
We are seeking a Data and Artificial Intelligence (AI) Capabilities Advisor to provide technical support and thought leadership to various DAF Data and AI strategy and implementation initiatives. The selected candidate will lead high\-impact studies and sponsor engagements that inform strategic investment, acquisition planning, policy and program decisions.
This role will provide objective expertise, independent analysis, and strategic guidance to the Office of the Assistant Secretary of the Air Force for Space Acquisition and Integration (SAF/SQ) and independent technical analysis and systems engineering insight to inform space acquisition, integration, and enterprise decision\-making involving data\-driven and AI\-enabled solutions.
The selected candidate will be required to work full\-time, onsite in Arlington, VA to include the Aerospace facility in Crystal City, the Pentagon and associated facilities.
What You’ll Be Doing
- Provide objective, non\-advocacy technical advice to the DAF on the application of AI and solutions across space systems, architectures, and acquisition portfolios.
- Assess technology readiness, implementation risk, and integration challenges for AI, machine learning, autonomy, and cyber technologies in space acquisition programs
- Support enterprise capability development and integration, including mission thread analysis and cross\-program integration studies and activities
- Advise on acquisition and integration strategies
- Develop technical roadmaps, trade studies, and decision\-quality products to support senior SAF/SQ, Space Force, and OSW stakeholders
- Coordinate with Aerospace technical experts, SSC, AFRL, USSF, and other FFRDC partners to ensure coherent, end\-to\-end technical insight
- Prepare and deliver clear, concise briefings and written assessments translating complex technical analysis into actionable recommendations
- Deliver results on time\-sensitive staff inquiries and provide technical support in the development of position papers and official responses to Congress, the Office of the Secretary of War, and the Intelligence Community (IC)
- The selected candidate must work effectively in an onsite environment with the customer and work with senior Government leaders, contractors, other government agencies, the various Aerospace program offices, and the Aerospace Engineering and Technical Group (ETG) to address issues and provide the customer with the best available technical analyses and recommendations to ensure coherent, end\-to\-end technical insight
What You Need to be Successful
*Minimum Requirements Senior Project Engineer – Artificial Intelligence and Data Science/Big Data Mining:*
- Bachelor’s degree in engineering, Computer Science, Data Science, or a related technical field
- 12 or more years of demonstrated technical depth in one or more of the following areas:
+ Artificial intelligence
+ Space systems engineering and architectures
+ Digital engineering and model\-based systems engineering (MBSE)
- Proven ability to work effectively with high\-functioning teams composed of, for example, government, civilians, military personnel, co\-located FFRDC, and SETA contractors
- Understanding of space acquisition, integration, and lifecycle processes within the Department of the Air Force or U.S. Space Force.
- Strong technical writing and executive briefing abilities
- This position requires the ability to obtain and maintain a DoD security clearance, issued by the US government. US citizenship is required to obtain a security clearance.
How You Can Stand Out
*It would be impressive if you have one or more of these:*
- Advanced degree in engineering, mathematics, science, computer science, or other technical discipline
- Prior experience supporting SAF/SQ, Space Systems Command (SSC), SAF/AQ, USSF headquarters, or DoW space organizations.
- Familiarity with Aerospace Corporation engineering practices, including mission assurance, architecture evaluation, and technical risk assessment.
- Experience with system security engineering for space system threats, including contested and degraded space environments.
- Experience with systems engineering for space system use of autonomy and AI with DoW Responsible AI
- Background in mission\-level modeling, architecture trades, or enterprise integration.
- Experience working in an FFRDC or trusted advisory environment.
- Experience in briefing senior Government leaders (General Officer and/or SES)
- Possess a current TS/SCI clearance
- A thorough understanding of the roles played by the Office of the Secretary of War; the Office of the Secretary of the Air Force; the United States Space Force, U.S. Space Command; the space system program offices; the defense agencies; the Joint Staff; and other government organizations in the acquisition, management, and operation of the national security space enterprise
We offer a competitive compensation package where you’ll be rewarded based on your performance and recognized for the value you bring to our business. The grade\-based pay range for this job is listed below. Individual salaries within that range are determined through a wide variety of factors including but not limited to education, experience, knowledge and skills.
(Min \- Max)
$166,400\.00 \- $249,600\.00
Pay Basis: Annual
Leadership Competencies
Our leadership philosophy is simple: every employee, regardless of level and role, can demonstrate leadership. At Aerospace, our commitment is our people. To cultivate our talent and ensure that we have a strong pipeline of future leaders, we want individuals who:
- Operate Strategically
- Lead Change
- Engage with Impact
- Foster Innovation
- Deliver Results
Ways We Reward Our Employees
During your interview process, our team will provide details of our industry\-leading benefits.
Benefits vary and are applicable based on Job Type. *A few highlights include:*
- Comprehensive health care and wellness plans
- Paid holidays, sick time, and vacation
- Standard and alternate work schedules, including telework options
- 401(k) Plan — Employees receive a total company\-paid benefit of 8%, 10%, or 12% of eligible compensation based on years of service and matching contributions; employees are immediately eligible and vested in the plan upon hire
- Flexible spending accounts
- Variable pay program for exceptional contributions
- Relocation assistance
- Professional growth and development programs to help advance your career
- Education assistance programs
- An inclusive work environment built on teamwork, flexibility, and respect
We are all unique, from various backgrounds and all walks of life, yet one thing bonds all of us to each other—the belief that we can make a difference. This core belief empowers us to do our best work at The Aerospace Corporation.
Equal Opportunity Commitment
The Aerospace Corporation is an equal opportunity employer. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, age, sex (including pregnancy, childbirth, and related medical conditions), sexual orientation, gender, gender identity or expression, color, religion, genetic information, marital status, ancestry, national origin, protected veteran status, physical disability, medical condition, mental disability, or disability status and any other characteristic protected by state or federal law. If you’re an individual with a disability or a disabled veteran who needs assistance using our online job search and application tools or need reasonable accommodation to complete the job application process, please contact us by phone at 310\.336\.5432 or by email at [email protected] . You can also review Know Your Rights: Workplace Discrimination is Illegal .
Salary Context
This $166K-$249K 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 The Aerospace Corporation, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. Disclosed range: $166K to $249K.
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.
The Aerospace Corporation AI Hiring
The Aerospace Corporation has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Crystal City, VA, US, El Segundo, CA, US, Springfield, VA, US. Compensation range: $104K - $257K.
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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