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Advanced Space · 2 hours ago

Machine Learning Engineer

Westminster, CO

Full-time

Onsite

Mid, Senior Level

$122K/yr - $144K/yr

5+ years exp

Advanced Space is leading humanity back to the Moon and pioneering innovative solutions in the space industry. They are seeking a Machine Learning Engineer responsible for designing, building, and testing machine learning models and systems to solve real-world problems in the aerospace domain.

Defense & Space

No H1B

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

Qualification

Machine LearningNeural NetworksAlgorithmsData StructuresPyTorchTensorFlowJaxAerospace EngineeringSoft Skills

Required

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