Machine Learning Engineer

Advanced Space•Westminster, CO

19d•$122,284 - $144,284•Onsite

About The Position

The team at Advanced Space is leading humanity back to the Moon and pioneering innovative solutions in the space industry. Based in Westminster Colorado, this incredible team is looking for a Machine Learning Engineer who’s eager to push the state of the art in the aerospace industry. ML Engineers at Advanced Space work closely with engineers in other disciplines (i.e., aerospace, systems). This position is responsible for designing, building, and testing machine learning models and systems that can solve real-world problems in domains such as spacecraft autonomy, anomaly detection and classification, natural language processing, uncertainty quantification, and multimodal signal detection & characterization. This position will be headquartered at our Westminster, Colorado offices and is primarily an on-premises position. Advanced Space exists to support the sustainable exploration, development, and settlement of space and operates with four core values: We are customer obsessed and mission focused; when we see it, we solve it; we are one team, motivated by our vision; and we bring technical excellence and inexhaustible curiosity.

Requirements

B.S. in Computer Science, Machine Learning, Software Engineering, Aerospace Engineering, or another physical science or engineering discipline or equivalent work experience.
5-8 years of relevant experience.
Handle a broad range of tasks, from bug fixes to feature development.
Software/algorithm component design.
A basic understanding of aerospace engineering.
Strong understanding of algorithms, data structures, and system design principles.
Has started to specialize in certain areas and contribute to process details for those areas.
Strong understanding of the basics of neural networks (i.e., feedforward, recurrence, convolution, attention).
Able to understand and implement new methods.
Familiarity with a variety of methods (i.e. reinforcement learning, supervised/unsupervised learning, principal components analysis, clustering, regression, classification).
Understands system design and architecture. Able to apply methods from other domains to our problems. Makes creative use of internet and LLM resources.
Confident in writing a neural network model from scratch
Makes effective use of tools to track model training.
Understands train/test/validation splits.
Understands when to be suspicious of a model's unexpectedly good results.
Proficient with PyTorch, TensorFlow, Jax, or equivalent.
US Person

Nice To Haves

Proficient in an engineering field (aerospace or another).
Confident in deploying a model to a server or edge device.

Responsibilities

Apply ML first principles and engineering intuition to projects such as fast & accurate models of physical systems, spacecraft navigation, spacecraft autonomy, signal extraction from noisy data, natural language processing, internal company tools, multi-agent reinforcement learning, and approximate optimal maneuver design.
Lead the development of advanced ML models and AI-driven solutions.
Research and implement emerging ML technologies and techniques.
Improve model interpretability, scalability, and efficiency.
Contribute to strategic decision-making and technical roadmaps.
Work with interdisciplinary, cross-functional teams to apply machine learning / artificial intelligence algorithms to a variety of problems in the space industry.
Find, digest and apply knowledge from research publications to assigned tasks.
Stay up-to-date on the latest research in the industry by reading technical papers and attending technical conferences.
Participate in technical interviews.