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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 Digital Innovation Division (DID) is accountable for generating and executing integrated strategies, providing governance, and managing internal investments that form the foundation of Aerospace’s digital transformation.
The Digital Innovation Division is seeking Senior Project Engineers to serve as technical project leads for initiatives within the corporate AI portfolio, reporting to the Director, AI Systems and Delivery. Each lead owns one initiative end to end: its technical approach, its requirements and architecture, its cost and schedule, and the working relationships with the engineers and subject matter experts whose work the capability depends on.
We expect to fill more than one of these positions. Current and planned initiatives include an agentic platform that gives AI controlled, secure access to subject matter expert tools, databases, physics models, and document collections, spanning component engineering, modeling and simulation, verification and validation, and spectral phenomenology; enterprise agentic AI, covering orchestration, tool and data integration, and agent lifecycle management at enterprise scale; AI for software engineering, covering agentic coding assistants and developer productivity across the engineering population; and modeling, simulation, and digital engineering tooling. Depth in one of these areas matters more than breadth across all of them, and assignment is determined during hiring based on your background.
This is a technical leadership position rather than a people-management position. You will lead a project team drawn from across the corporation, hold the technical decisions for your initiative, and remain close enough to the work to judge whether what is being built is sound. You will be expected to tell us what should not be built as readily as what should.
Work Model:
This is a full-time position based 100% onsite in Colorado Springs, Co, Chantilly, Va, or El Segundo, Ca.
What Corporate Skills You’ll Bring
- Ability to build trusted working relationships with engineers, subject matter experts, and senior stakeholders
- Leading and delivering complex technical initiatives across classified and unclassified environments
- Taking new and emerging initiatives from concept through execution when requirements are still forming
- Bringing together cross functional teams, technology, and mission priorities to drive impact.
- Understanding of evolving priorities, and translate mission and business needs into technical approaches and delivered capability.
What You’ll Be Doing:
- Serve as the technical project lead for an assigned AI initiative, owning its technical approach, requirements, architecture, roadmap, and technical dependencies.
- Lead the project team day to day, including engineers, subject matter experts, and partner organizations who do not report to you, and set the technical direction they work to.
- Own cost and schedule performance for your initiative, including estimates, milestone commitments, variance, and the funding requests that support it.
- Decompose a loosely defined capability goal into scoped increments that deliver something usable, and sequence them so value arrives before the full vision does.
- Define what good looks like for your capability and measure it, including evaluation criteria, acceptance tests, and the performance thresholds a capability must meet before it reaches users.
- Identify, track, and retire technical risk, and raise disconnects that cannot be resolved at the project level rather than absorbing them quietly.
- Work with subject matter experts to capture how expert work is actually performed, and translate that into tools, data, and agent behavior that engineers will trust and use.
- Partner with platform, data, security, and accreditation teams to field capability in the environments where the work happens, including accredited networks.
- Represent your initiative in technical reviews, program management reviews, and stakeholder forums, and brief its status honestly, including what is behind and why.
- Document the technical baseline and keep it current, so the initiative does not depend on what is in your head.
- Mentor engineers on the project and grow AI delivery skill across the team.
What You Need to be Successful: Senior Project Engineer-
Minimum Requirements:
- Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, Data Science, Statistics, Information Systems, Operations, Aerospace Engineering, Physics, STEM or a related technical field, or equivalent work experience.
- 8 years of progressive technical experience in AI/ML engineering, software or systems engineering, modeling and simulation, or a comparable engineering discipline, including delivering working capability to real users.
- Demonstrated experience leading a technical project or team, including accountability for technical approach, cost, and schedule, without relying on formal reporting authority over the team.
- Working proficiency in Python and practical experience building with modern AI/ML tooling, which may include large language model APIs, agent orchestration frameworks, retrieval augmented generation, evaluation harnesses, or classical ML and simulation frameworks.
- Ability to reason about system design and make defensible architecture decisions, including data flow, integration with existing tools and databases, and the tradeoffs between what is fast to build and what is sustainable.
- Working understanding of cloud platforms (Azure, AWS, or GCP) and of how capability moves from prototype to a supported production environment.
- Demonstrated ability to work directly with subject matter experts or customers to elicit requirements, and to translate domain expertise into technical requirements.
- Strong written and verbal communication, including the ability to brief technical status and technical risk to leadership clearly and without overstating progress.
- Willing to travel approximately 25% of the time to support customer meeting and technical collaboration.
- This position requires ability to obtain and maintain a security clearance, which is issued by the U.S. government. U.S. 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 a STEM or other technical discipline.
- 12+ years of technical experience, including leading multi-person technical projects to delivery.
- Direct mission experience in the national space domain, missile defense, or remote sensing.
- Experience building agentic AI systems that call tools, query databases, or drive physics models, and the evaluation work required to show they are reliable enough to use.
- Experience deploying AI capability that engineers adopted and kept using, with evidence of sustained use rather than a successful demonstration.
- Depth in one of these engineering domains: component engineering, modeling and simulation, verification and validation, or spectral phenomenology.
- Experience with agentic coding assistants or developer productivity tooling at organizational scale.
- Experience delivering capability on accredited networks, or working within authorization to operate and continuous monitoring processes.
- Experience establishing evaluation criteria or acceptance gates for AI capability, including deciding that something was not ready to field.
- Experience standing up a capability in its earliest phase, before requirements, funding, or customer scope were fully defined.
- Current DoD TS and SSBI/SCI 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)
$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: $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. Disclosed range: $166K to $249K.
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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