Interested in this AI/ML Engineer role at The Aerospace Corporation?
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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 Enterprise Effects Division (EED) drives integration of Aerospace’s strategic capabilities across the national space enterprise. The division leverages a broad array of engineering disciplines against a dynamic portfolio of programs, customers, and national challenges. EED spans the capability lifecycle from concept and architecture design to performance assessment to application of space and space\-enabled capabilities.
The new hire will join the Space Planning, Readiness, and Operations (SPRO) Department team to provide operationally focused space solutions for warfighter and end user in support of our Department of War (DoW), Intelligence Community (IC), and civil customers. We serve as the EED and ETG entry point for wargaming and exercises, space operations planning, and operationally relevant solutions support to connect corporate technical capabilities and operational requirements, integrate capabilities across the space enterprise, and educate the workforce on space operations and warfighting. Do you want to have a positive impact on the present and future space enterprise? Join us as a Space Artificial Intelligence (AI) Operational Engineer on the Space Planning, Readiness, and Operations Department team at The Aerospace Corporation!
Work Model
- 100% Onsite: This is a full\-time position based in Colorado Springs, CO, which requires 100% onsite work.
What You’ll Be Doing
- Designing vignettes and use cases for the adoption and implementation of artificial intelligence (AI) and machine learning (ML) concepts.
- Partnering with organizations and experts within the company to drive AI/ML\-enabled solutions to operationally relevant challenges.
- Providing thought leadership to corporate AI leaders and government stakeholders to develop strategic plans that integrate AI/ML into elements of the customer’s architecture.
- Exhibiting leadership by bridging the gap between operational needs and requirements and AI/ML technical solutions and educating Aerospace staff on operational use cases.
- Working across space mission and IC boundaries to identify common program challenges and risks, provide enterprise recommendations, foster innovative partnerships, and lead issues to closure.
- Working closely and effectively with internal and external partners in integrated product teams that include government, industry, FFRDC members, and collaboration across Aerospace.
- Communicating effectively with customers and management through timely reporting of technical issues, analysis reports, and recommendations.
What You Need to be Successful – Engineering Specialist
*Minimum Requirements:*
- Bachelor’s degree from a recognized institution in engineering, mathematics, technical science, or relevant field.
- 8 or more years of professional experience in space operations, planning, or warfighting roles.
- A keen understanding of how AI/ML principles may enable future operations.
- Energy and willingness to learn about AI/ML principles and identify good matches between customer needs and AI/ML technical solutions.
- Ability to thrive in a dynamic, exciting environment with rapidly evolving, competing priorities.
- Willing to travel occasionally to work location or customer site , as required.
- Active Top Secret security clearance and ability to obtain and maintain access to Sensitive Information (SCI), which is issued by the U.S. government. U.S. citizenship is required to obtain a security clearance.
*In addition to the above, the minimum requirements for the* *Senior Engineering Specialist* *include:*
- 12 or more years of relevant career experience.
- Demonstrated blend of operational, warfighting, and/or planning experience and data science and AI/ML technical growth to include leading projects.
How You Can Stand Out
*It would be impressive if you have one or more of these:*
- Advanced degree from a recognized institution in engineering, mathematics, technical science, or relevant field.
- Experience in applying AI/ML principles to DoD/DoW or industry projects.
- General awareness of cloud technologies and platforms (e.g., AWS, Azure, Platform One).
- Familiarity with latest AI advancements in data management, automation, governance, and operations.
- General awareness of the external landscape with respect to AI/ML.
- Experience developing software applications and tools using modern object\-oriented or scripting languages.
- Experience developing dashboard displays for complex problem sets.
- Experience leading and completing projects within a matrixed organization.
- Experience with different space operations mission areas/functions such as electronic warfare (EW), orbital warfare (OW), missile warning missile tracking (MW/MT), space domain awareness (SDA), intelligence, surveillance, and reconnaissance (ISR), command and control (C2\), and position, navigation, and timing (PNT).
- Excellent oral and written communication skills that span technical and operational subjects.
- Active Top Secret security clearance with SCI access.
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)
$117,300\.00 \- $175,900\.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 $117K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($146K) sits 19% below the category median. Disclosed range: $117K to $175K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
The Aerospace Corporation AI Hiring
The Aerospace Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Colorado Springs, CO, US. Compensation range: $175K - $175K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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