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About This Role
Advanced Space | Machine Learning Engineer (5–8 Years) | Full-time | Hybrid
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 Machine Learning Engineer with 5–8 years of experience to develop innovative ML-driven capabilities that support spacecraft missions, autonomy, navigation, mission planning, and advanced engineering solutions.
This is a hands-on technical role focused on translating complex mission and engineering challenges into practical, data-driven solutions. You'll take ownership of technically complex projects from problem formulation and model development through quantitative evaluation, integration, and operational deployment. You'll work with modern machine learning techniques, including statistical learning, probabilistic modeling, optimization, deep learning, and reinforcement learning, to solve real-world aerospace challenges.
We're looking for someone who enjoys tackling ambiguous technical problems, has a strong foundation in machine learning and software engineering, and is excited to collaborate across disciplines to develop capabilities that support real space missions.
This position is open to U.S. Persons (U.S. citizens or lawful permanent residents) only. Visa sponsorship is not available.
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 seeing your work fly in space, you'll fit right in.
What You'll Actually Do
Develop machine learning solutions for complex engineering challenges.
Translate mission, operations, and engineering needs into well-defined ML and data-driven problems. Establish success metrics, baselines, datasets, evaluation plans, and quantitative acceptance criteria to develop solutions that address real-world mission requirements.
Design and implement ML-enabled capabilities.
Select, develop, evaluate, and maintain machine learning solutions that support spacecraft mission planning, operations, autonomy, navigation, physical-system modeling, signal extraction, and internal engineering workflows. Apply appropriate methods based on mission needs, data availability, computational constraints, and operational requirements.
Own technically complex projects from concept to deployment.
Take ownership of technical work packages from initial problem formulation through implementation, integration, documentation, and operational handoff. Define technical approaches, assess trade-offs, identify risks, and communicate architectural decisions and recommendations to stakeholders.
Build reliable and reproducible ML workflows.
Develop and maintain end-to-end machine learning workflows, including data curation and validation, experiment tracking, model and data versioning, configuration management, automated regression testing, and performance monitoring. Apply modern software engineering practices to ensure solutions are maintainable, scalable, and reliable.
Evaluate model performance and validate results.
Design rigorous evaluation strategies and domain-appropriate metrics to assess nominal, edge-case, and off-nominal performance. Identify data leakage, distribution shifts, and other factors that could impact model reliability. Use quantitative analysis and experimentation to validate model performance and inform technical decisions.
Integrate ML capabilities into aerospace systems.
Collaborate with navigation, mission design, flight software, systems engineering, and operations teams to integrate machine learning solutions into broader engineering architectures and workflows. Ensure ML capabilities align with mission requirements, system constraints, and operational needs.
Research and apply emerging technologies.
Read, synthesize, and apply relevant technical literature, emerging research, and innovative methodologies in machine learning, optimization, and autonomy. Evaluate new approaches and identify opportunities to advance Advanced Space's technical capabilities.
Communicate technical findings and recommendations.
Document technical approaches, assumptions, results, limitations, and recommendations. Present findings through design reviews, technical documentation, and stakeholder discussions, translating complex ML concepts into clear, actionable insights for multidisciplinary teams.
Leverage modern AI-assisted engineering tools.
Use company-approved AI-assisted and agentic engineering tools responsibly to support software development, documentation, research, and analysis. Critically evaluate generated outputs and apply appropriate security, source-provenance, reproducibility, and technical-validation practices.
Who Thrives Here
- You have a B.S. in Computer Science, Machine Learning, Software Engineering, Aerospace Engineering, or another relevant engineering, physical-science, or quantitative discipline. Equivalent relevant experience may be considered.
- You have 5–8 years of professional experience developing and integrating machine learning, optimization, statistical, or data-driven engineering capabilities.
- You have demonstrated experience owning technical problems from initial formulation through implementation, quantitative evaluation, documentation, and stakeholder communication.
- You are proficient in Python and modern software engineering practices, including version control, code reviews, automated testing, debugging, and performance profiling.
- You have experience with at least one modern ML framework, such as PyTorch, JAX, TensorFlow, or an equivalent, including developing custom models, loss functions, data pipelines, training loops, and inference workflows.
- You understand common machine learning model families, including neural network architectures, and can select or adapt approaches based on data availability, computational constraints, mission requirements, and operational risk.
- You have experience developing reproducible, end-to-end ML workflows, including data validation, experiment tracking, model and data versioning, integration testing, and model evaluation.
- You have working knowledge of at least one aerospace domain, such as astrodynamics, spacecraft systems, navigation, mission design, or flight and ground software, or the ability to rapidly build expertise in these areas.
- You are familiar with machine learning applications for physical or engineered systems, reinforcement learning, or decision-making methods.
- You can effectively communicate complex technical concepts and collaborate with multidisciplinary engineering teams.
- You take ownership of your work, approach challenges with curiosity, and are comfortable navigating technical ambiguity.
Bonus Points if You Have Experience With
- An M.S. or Ph.D. in Aerospace Engineering, Computer Science, Artificial Intelligence, Robotics, or a related field.
- Applying model-based or model-free reinforcement learning, model predictive control (MPC), Markov decision processes (MDPs), partially observable Markov decision processes (POMDPs), or hybrid planning approaches to physical systems.
- Developing autonomy architectures spanning perception, estimation and navigation, planning and scheduling, control, and fault management.
- Building simulation or digital-twin environments for model development, evaluation, and sim-to-real transfer.
- Integrating ML-enabled capabilities into guidance, navigation, and control (GN&C), mission design, navigation, flight software, or systems-engineering workflows.
- Applying optimization, probability, statistics, and rigorous experimental design to complex engineering problems.
- Using probabilistic modeling or Bayesian inference to address engineering challenges.
- Communicating complex technical concepts across machine learning, navigation, mission design, flight software, operations, and systems engineering.
- Using agentic AI tools to support engineering workflows while maintaining technical accuracy, security, and reproducibility.
Success is Measured By
- Delivering validated, reliable ML capabilities that address mission and engineering requirements.
- Developing and integrating machine learning solutions that support spacecraft autonomy, navigation, mission planning, and operations.
- Building reproducible ML workflows that improve model development, testing, evaluation, and deployment.
- Demonstrating measurable model performance through rigorous experimentation and quantitative evaluation.
- Effectively integrating ML capabilities into broader aerospace systems and engineering workflows.
- Taking ownership of complex technical challenges and delivering solutions from initial concept through implementation and operational handoff.
- Communicating technical findings, limitations, risks, and recommendations clearly to engineering teams and stakeholders.
- Contributing to the continued growth of Advanced Space's machine learning, autonomy, and engineering capabilities.
Why Join Advanced Space
- Work on real missions that are shaping the future of lunar and deep-space exploration.
- Develop machine learning and autonomy solutions that support spacecraft operations and next-generation space technologies.
- Collaborate with experts in machine learning, navigation, mission design, and aerospace engineering.
- Apply cutting-edge ML techniques to challenging, real-world engineering problems.
- Be part of a growing company where your technical contributions have a direct impact on mission success.
- Join a team that's passionate about delivering innovation to orbit—and beyond.
Compensation & Benefits
- Base Salary: $124K -$171K (based on experience, qualifications, and location)
- Signing bonus
- Quarterly performance bonuses
- Company-sponsored medical benefits and 401(k)
- Flexible time off
- Relocation assistance
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 $124K-$171K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($147K) sits 30% below the category median. Disclosed range: $124K to $171K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Advanced Space AI Hiring
Advanced Space has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Westminster, CO, US. Compensation range: $171K - $171K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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