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We're going to the Moon. Think you've got what it takes?
About the Role
At Advanced Space, we're enabling humanity's return to the Moon and building the technologies that will take us to Mars and beyond. We're looking for a Deputy, AI Integration & Assurance to accelerate the responsible adoption of artificial intelligence across engineering, mission operations, and business functions — identifying where AI can transform how we work, building teams' capability to use it well, and establishing methods to verify that AI systems deliver the intended result safely and reliably.
This is not a conventional HR or corporate-training role. It sits at the intersection of organizational transformation, internal product development, systems thinking, AI evaluation, and mission assurance. You'll work directly with engineers, operators, leaders, and domain experts to translate real work into trustworthy human-AI systems.
Both onsite and remote candidates will be considered. If remote, regular on-site presence at our Westminster, CO headquarters, planned as a consolidated multi-day visits will be required, plus additional travel as needed. You'll report to the Chief AI Officer.
About Advanced Space
Advanced Space exists to enable the sustainable exploration, development, and settlement of space through innovative software, mission services, and technology solutions. As the owner and operator of NASA's CAPSTONE™ mission and the Prime Contractor for AFRL's Oracle mission, we're helping shape the future of cislunar exploration while supporting commercial, civil, and national security customers.
Our team combines deep technical expertise with an entrepreneurial mindset. We move quickly, collaborate across disciplines, and empower every engineer to make meaningful contributions. If you're passionate about solving challenging problems and making great work visible to the world, you'll fit right in.
What You'll Actually Do
Drive AI adoption across the company.
Partner with engineering, mission operations, and business teams to find workflows where AI can materially improve speed, quality, capability, or reliability — and help teams rethink how the work gets done, rather than merely automate current practice. Design and lead practical, role-specific AI adoption programs that build durable capability across the company, and develop methods to measure adoption, workflow impact, quality, reliability, and return on investment.
Build and own our internal AI products.
Work with technical counterparts to define requirements and priorities for specialized internal AI products, tools, agents, and knowledge systems. Lead discovery, prototyping, user feedback, and iterative improvement for internal AI-enabled products.
Own AI verification and assurance.
Establish and own the company's framework for autonomous verification of engineering analyses, operational work, software, and AI-agent outputs, in partnership with engineering leads who own domain correctness. Design evaluation approaches that test whether AI systems achieve the underlying intent of an objective, not merely a superficially correct result. Identify and mitigate risks including hallucination, overreliance, metric gaming, weak evaluation design, and unsafe tool use.
Set the standard for safe, trustworthy AI use.
Establish organizational practices for safe human oversight, escalation, accountability, and continuous improvement of AI-enabled workflows. Translate AI capabilities, limitations, and risks into clear operational guidance for both technical and nontechnical audiences.
Stay ahead of the field.
Stay current on emerging AI capabilities, evaluation methods, agentic systems, and relevant practices in high-reliability industries. Build strong partnerships with technical AI/platform leaders, subject-matter experts, and functional leaders across Advanced Space.
Who You'll Work With
You'll work closely with engineering leadership, spacecraft and mission operations personnel, software and platform teams, systems engineers, and security and IT. You'll partner with the technical leaders responsible for AI infrastructure, software implementation, data, and security — this role is not expected to be the sole technical architect for those systems.
Who Thrives Here
- An advanced degree or equivalent experience in human factors, systems thinking, organizational psychology, education, social sciences, management science, or a related field.
- You've led complex organizational change, capability-building, program design, or cross-functional initiatives.
- You have an exceptional ability to understand how people perform complex work and turn that understanding into improved processes, tools, or learning systems.
- You communicate well in writing and in person, and can work credibly with both technical experts and senior leaders.
- You've designed assessments, evaluations, experiments, or other evidence-based methods to measure outcomes.
- You bring sound judgment about risk, incentives, unintended consequences, and the limits of automation.
- You operate effectively in an ambiguous, rapidly evolving technical environment.
- You have 15+ years of relevant experience across the skillsets described above.
Bonus Points if You Have Experience With
- Applying generative AI or agentic systems to real organizational workflows.
- Engineering, mission operations, aerospace, defense, safety-critical systems, or other high-reliability environments.
- Product discovery, internal product development, user research, or service design.
- AI evaluation, human-in-the-loop systems, model behavior, or AI safety concepts.
- Teaching, coaching, facilitating workshops, or building learning programs for technical professionals.
- Lightweight technical prototyping, or working closely with software, data, and AI engineers.
- A PhD or equivalent research experience in a relevant social-science, education, behavioral-science, or human-systems discipline.
Success is Measured By
- Repeatable evaluation and verification practices for AI-enabled work
- Increased effective AI adoption — measured by sustained use and workflow impact, not just tool access
- Measurable improvements in speed, quality, and reliability across selected engineering, operations, and business workflows
- Delivery and adoption of internal AI products that address meaningful team needs
- Clear reduction of known AI risks through better task design, oversight, evaluation, and operational controls
- A growing company capability to design, deploy, and improve trustworthy human-AI systems
Why Join Advanced Space
- Shape how a company doing genuinely distinctive technical work adopts AI — from the ground up, with real ownership.
- Work directly with engineers, operators, and mission leaders solving some of the aerospace industry's hardest problems, and help them do it faster and more safely with AI.
- Play a foundational role in establishing how Advanced Space verifies that AI systems can be trusted with real engineering and mission work.
- Report directly to the Chief AI Officer and help shape a growing function from its earliest days.
- Join a team that moves quickly, thinks rigorously, and cares as much about getting AI right as about using it.
Compensation & Benefits
- Base Salary: $150,000 - $190,000 (subject to experience and location)
- Signing bonus
- Quarterly performance bonuses
- Company-sponsored medical benefits and 401(k)
- Flexible time off
- Relocation assistance (if applicable)
Full benefits details are provided during the interview process.
Advanced Space is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive workplace for all employees. Employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, veteran status, or any other protected characteristic under applicable law.
Salary Context
This $150K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Advanced Space, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $150K to $190K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Advanced Space AI Hiring
Advanced Space has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Westminster, CO, US. Compensation range: $171K - $190K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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