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About This Role
Application close date:
Applications will be accepted on an ongoing basis until the requisition is closed.
At Blue Origin, we envision millions of people living and working in space for the benefit of Earth. We’re working to develop reusable, safe, and low\-cost space vehicles and systems within a culture of safety, collaboration, and inclusion. Join our team of problem solvers as we add new chapters to the history of spaceflight!
This role is part of the Lunar Permanence business unit, which develops Blue Origin’s Blue Moon landers and related products. To further Blue Origin's mission of millions of people living and working in space for the benefit of Earth, we are building sustainable infrastructure for our transport of crew and cargo from Earth to the lunar surface.
As a Lunar AI Solutions Engineer, you will accelerate Blue Origin's mission by designing, building, and deploying AI solutions that drive measurable adoption across Lunar Permanence engineering and program teams. In this role, you will serve as both a hands\-on solutioneer and technical guide, creating production\-grade AI agents and AI\-automated enhancements while supporting teams in putting those tools to work. You will evangelize AI tools and processes, identify opportunities for AI\-enabled acceleration, and empower the organization to implement these solutions faster.
A successful candidate will:
- Rapidly iterate AI solutions from definition through deployment and hands\-on training
- Work directly with engineers to understand workflows and deliver impactful AI tools
- Communicate effectively and clearly to both engineers and business stakeholders
- Build trust across programs by creating results and scaling from demonstrated success
Key Responsibilities:
- Design, build, and ship AI tools and agent architectures that can be adopted and adapted across Lunar Permanence without requiring individual re\-engineering
- Embed directly with Lunar teams to identify high\-value use cases, design custom solutions, and ensure deployments follow best practices
- Coordinate and support AI training programs , office hours, workshops, and onboarding that bring the full organization onto the AI adoption curve
- Serve as a hands\-on community builder across the LunarAI Community of Practice; surface AI wins and propagate best practices across the organization
- Evaluate and investigate emerging AI technologies, pilot promising tools, and support the organizational change efforts required to adopt them at scale
Minimum Qualifications:
- Extreme passion for AI/ML as an enabler in complex product development
- Hands\-on AI application build and support experience, has built and shipped real solutions with LLMs (Agents, MCP, Skills, RAG, orchestration)
- Python proficiency, competent and productive in the core language of the AI ecosystem
- Demonstrable comfort with AI development tools and interfaces (Claude Code, Cursor, Windsurf, Gitlab, Databricks, etc.)
- Highly comfortable speaking to and leading groups through communication \& teaching ability, can run a room, deliver training, and translate concepts for both technical and non\-technical stakeholders
- Has driven tool adoption and solution changes as either a champion of new initiatives or grassroots\-driven opportunities
Preferred Qualifications:
- 2\+ years in relevant engineering with space launch vehicle and/or in\-space systems experience across disciplines (requirements, design, analysis, manufacturing, testing, integration, operations)
- B.S. in computer science, data science, or STEM field
- 1\+ years creating and/or working on ML / NLP / LLM / MCP / Agentic solutions
- Experience utilizing LLM fundamentals, ML Frameworks, and Deep Learning techniques
- Experience with deployment via GitLab, AWS, Docker, and Kubernetes
- Experience with data automation and data integrations: APIs, webhooks, data pipelines
Experience with Agile, IMP\-level scheduling, systems engineering, and risk/configuration/ requirements management
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Base Pay Range for:
CO applicants is $123,714\.00 \- $173,199\.60
Other site ranges may differ
Culture Statement
Don’t meet all desired requirements? Studies have shown that some people are less likely to apply to jobs unless they meet every single desired qualification. At Blue Origin, we are dedicated to building an authentic workplace, so if you’re excited about this role but your past experience doesn’t align perfectly with every desired qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles.
Export Control Regulations
Applicants for employment at Blue Origin must be a U.S. citizen or national, U.S. permanent resident (i.e. current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.
Background Check
- Required for all positions: Blue’s Standard Background Check
- Required for Certain Job Profiles: Defense Biometric Identification System (DBIDS) background check if at any time the role requires one to be on a military installation
- Required for Certain Job Profiles: Drivers who operate Commercial Motor Vehicles with a Gross Vehicle Weight (GVW), Gross Vehicle Weight Rating (GVWR) or combination of power unit and trailer that meets or exceeds 10,001 lbs. and/or transports placardable amounts of hazardous materials by ground in any vehicle on a public road while in commerce, may be subject to additional Federal Motor Carrier Safety Regulations including: Driver Qualification Files, Medical Certification (obtained before onboarding), Road Test, Hours of Service, Drug and Alcohol Testing (CDL drivers only), vehicle inspection requirements, CDL requirements (if applicable) and hazardous materials transportation/shipping training.
- Required for certain Job Profiles: Ability to obtain and maintain Merchant Mariner Credential, which includes pre\-employment and random drug testing as well as DOT physical
Benefits
- Benefits include: Medical, dental, vision, basic and supplemental life insurance, paid parental leave, short and long\-term disability, 401(k) with a company match of up to 5%, and an Education Support Program.
- Stock Options for all regular employees (working at least 20 hours/week)
- Paid Time Off: Up to four (4\) weeks per year based on weekly scheduled hours, and up to 14 company\-paid holidays.
- Dependent on role type and job level, employees may be eligible for benefits and bonuses based on the company's intent to reward individual contributions and enable them to share in the company's results, or other factors at the company's sole discretion. Bonus amounts and eligibility are not guaranteed and subject to change and cancellation. Please check with your recruiter for more details.
Equal Employment Opportunity
Blue Origin is proud to be an Equal Opportunity/Affirmative Action Employer and is committed to attracting, retaining, and developing a highly qualified and dedicated work force. Blue Origin hires and promotes people on the basis of their qualifications, performance, and abilities. We support the establishment and maintenance of a workplace that fosters trust, equality, and teamwork. We provide all qualified applicants for employment and employees with equal opportunities for hire, promotion, and other terms and conditions of employment, regardless of their race, color, religion, sex, sexual orientation, gender identity, national origin/ethnicity, age, physical or mental disability, genetic factors, military/veteran status, or any other status or characteristic protected by federal, state, and/or local law. Blue Origin will consider for employment qualified applicants with criminal histories in a manner consistent with applicable federal, state, and local laws, including the Washington Fair Chance Act, the California Fair Chance Act, the Los Angeles Fair Chance in Hiring Ordinance, and other applicable laws.
Affirmative Action and Disability Accommodation
Applicants wishing to receive information on Blue Origin’s Affirmative Action Plans, or applicants requiring a reasonable accommodation in order to participate in the application and/or interview process, please contact us at [email protected] . Please note this is a publicly managed inbox. Please do not include any personal medical information in your request.
Salary Context
This $123K-$173K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Blue Origin, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($148K) sits 32% below the category median. Disclosed range: $123K to $173K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Blue Origin AI Hiring
Blue Origin has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $173K - $173K.
Location Context
AI roles in Denver pay a median of $201,050 across 48 tracked positions. That's 8% below the national 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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