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.
Information Systems and Cyber Division (ISCD) staff couple the latest in information system technologies, such as elastic compute clouds, containerization, microservices, real-time operating systems, and visualization frameworks, with expertise in cyber security, software architecture, software engineering, data science, Artificial Intelligence, process improvement, and software development to deliver responsive, resilient, high-performance software intensive systems to our Intelligence Community, DoD, and civilian customers.
We are seeking a creative and enthusiastic Machine Learning Engineer to join a team of engineers, data scientists, and programmers with a passion for researching, prototyping, understanding, and building AI and data enabled tools across the space enterprise. We are a growing, innovative, and collaborative department that makes meaningful contributions to National Security Space (AF, NRO, etc.), and Civil and Commercial customers (NASA, MDA, DHS, etc.).
This position is for the Machine Learning Engineering section, where we focus on translating cutting-edge AI research into robust, scalable, and production-ready machine learning solutions. We bridge the gap between theoretical ML solutions and real-world impact by optimizing and scaling ML models, designing and building robust ML software architectures, providing expertise in harnessing the latest advancements in compute hardware, and implementing MLOps and Trusted AI best practices.
This is a full-time position based in Chantilly, VA which requires 100% onsite work.
What You’ll Be Doing
Supporting customers from National Security Space (AF, NRO, etc.) to civil (NASA, NOAA, etc.) and commercial (commercial space, autonomous vehicles, etc.)
- A3i pplies data science and AI knowledge across the space enterprise, to Aerospace enterprise capabilities, and towards corporate workforce development and strategic focus areas
- Machine Learning Engineers learn and develop their skills by working on teams spanning disciplines, experience levels, and organizational boundaries.
- Primary functions include:
- Evaluation of technologies for use in scalable and resilient mission-critical applications in a production environment
- Coordinated development and execution of AI/ML experiments
- Coordinated development of proof-of-concept infrastructure configuration and software prototypes
- Collaboration with small, innovative teams to deliver features and products
- Written and verbal presentation of results to team members and stakeholders
- Reinforcing an environment of learning and progress with team members and others
- Focus on accountability and innovation in leadership competency development
- Duties, responsibilities and activities may change, or new ones may be assigned as needed
- Machine learning engineers must maintain a commitment to ongoing learning in order to stay current with government missions and the ever-changing climate of data science and AI. This requires both ongoing education in relevant domains, such as physics, math, and/or computer science, as well as an understanding of the existing systems and future objectives of government missions.
What You Need to be Successful
Minimum Requirements for the Machine Learning Engineer- Senior Member of Technical Staff
- Bachelors or Master’s degree in Computer Science, Computer Information Systems, Electrical or Computer Engineering or related technical field(s)
- 5 or more years of experience in Data Scientist or Machine Learning Engineer or AI/ML Researcher role in any domain
- Strong proficiency in at least two different programming languages (e.g. Python, R, C/C++), and ML frameworks including familiarity with machine learning and deep learning libraries in Python
- Hands on experience with container orchestration tooling(Docker, Kubernetes, etc.)
- Experience with and understanding of software engineering concepts with an AI focus (MLOps/DevOps, ML Development Lifecycle, data structures, etc.)
- Working knowledge of Unix/Linux operating systems
- This position requires ability to obtain and maintain a security clearance, which is issued by the US government. U.S citizenship is required to obtain a security clearance.
In addition to the above, the minimum requirements for the Machine Learning Engineer- Engineering Specialist :
- 8 or more years of experience in Data Scientist or Machine Learning Engineer or AI/ML Researcher role in any domain
- Demonstrated experience architecting, designing, or implementing enterprise-scale AI/ML solutions with multiple tenants and cloud integration
- Hands on Experience with MLOps and Data Engineering tools (e.g., MLFlow, Kubeflow, Neptune, Airflow, DVC)
How You Can Stand Out
It would be impressive if you have one or more of these:
- Advanced degree in Computer Science, Computer Information Systems, Electrical or Computer Engineering, or related field(s)
- Experience in designing or managing enterprise-level ML platforms and production-grade ML models at scale
- Familiarity with High Performance Computing hardware for ML (GPUs, TPUs), CUDA programming, and advanced ML optimization techniques and architectures
- Demonstrated ability to exercise judgement and critical thinking in a scientific discipline
- Experience implementing and guiding teams toward software development best practices
- Experience in SQL, NoSQL, Cypher and other big data querying languages
- Experience with big data frameworks (Hadoop, Spark, Flink etc.)
- Familiarity with data pipelining and streaming technologies (Apache Kafka, temporal, etc.)
- Demonstrated contributions to open-source software repositories (github, kaggle, etc.)
- Experience deploying ML models on cloud platforms (AWS, Azure, etc.)
- Domain expertise relevant to one or more customer organization mission areas (USSF, NRO, etc.)
- Active security clearance
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)
$110,400.00 - $193,500.00 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 $110K-$193K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($151K) sits 28% below the category median. Disclosed range: $110K to $193K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
The Aerospace Corporation AI Hiring
The Aerospace Corporation has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Colorado Springs, CO, US, Chantilly, VA, US, Chantilly, VA. Compensation range: $193K - $249K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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