Machine Learning Engineer (5-8 yrs)
About this role
The Machine Learning Engineer at Advanced Space is responsible for developing innovative, data-driven machine learning capabilities that directly support humanity's return to the Moon and future missions to Mars. The day-to-day work involves taking complex mission and engineering challenges and translating them into practical, data-driven machine learning problems. Engineers will own the entire lifecycle of technically complex projects, starting from initial problem formulation and model development through to quantitative evaluation, integration, and operational deployment in space systems. This role requires a blend of deep machine learning expertise and strong software engineering fundamentals to ensure solutions are reliable and scalable.
This position exists to build the core technologies necessary for sustainable space exploration, including autonomy, navigation, mission planning, and advanced engineering solutions. The successful candidate will operate within a multidisciplinary environment, collaborating closely with teams in navigation, flight software, and systems engineering. The ideal candidate thrives on tackling ambiguous technical problems, enjoys applying advanced ML techniques like deep learning and reinforcement learning to real-world aerospace constraints, and possesses an entrepreneurial mindset to move quickly and deliver tangible results.
Candidates who excel in this role are those who are technically grounded, enjoy seeing their work operate in space, and are adept at communicating complex technical findings to diverse stakeholders. They must be comfortable defining success metrics, managing data pipelines, and ensuring that ML solutions adhere strictly to mission requirements and system constraints.
What you will do
- Develop machine learning solutions designed to address complex engineering challenges within space missions.
- Translate operational and engineering requirements into clearly defined machine learning problems.
- Establish success metrics, baselines, datasets, and quantitative acceptance criteria for all developed solutions.
- Design and implement ML-enabled capabilities for spacecraft mission planning, autonomy, and navigation.
- Select, develop, evaluate, and maintain machine learning solutions across various aerospace applications.
- Own technically complex projects from initial concept through to final deployment and operational handoff.
- Build and maintain end-to-end ML workflows, including data curation, versioning, and performance monitoring.
- Collaborate with various engineering disciplines to integrate ML solutions into broader system architectures.
What you need
- Possesses 5 to 8 years of professional experience in machine learning engineering.
- Strong foundation in both machine learning techniques and software engineering principles is required.
- Experience in applying statistical learning, probabilistic modeling, optimization, deep learning, and reinforcement learning is necessary.
- Proven ability to take ownership of technical projects from problem formulation to operational deployment.
- Demonstrated skill in designing rigorous evaluation strategies and using domain-appropriate metrics to assess model performance.
- Ability to apply appropriate ML methods based on data availability, computational constraints, and operational requirements.
- Proficiency in applying modern software engineering practices to ensure solutions are maintainable and scalable.
- Experience in integrating ML capabilities into complex aerospace systems is essential.
Nice to have
- Experience with signal extraction techniques
- Familiarity with mission planning systems
Benefits
- Salary range of $124K - $171K
Skills and tech stack
Questions candidates ask
What is the remote policy for this role?
The job is listed as Full-time and is located in Westminster, CO. The posting does not specify remote work arrangements.
What is the salary range for this position?
The stated salary range for this Machine Learning Engineer role is $124K to $171K.
What is the primary tech stack used?
The role involves modern machine learning techniques including statistical learning, probabilistic modeling, optimization, deep learning, and reinforcement learning.
How is collaboration handled within the team?
Engineers will collaborate across disciplines, including navigation, mission design, flight software, and operations teams, to integrate ML solutions.