LazyApply

1-10 employees

Modular building solutions, modular buildings Ireland

4 days ago

Westminster, Colorado - Remote

Full-time

Mid Level

Machine Learning Engineer

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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 position is responsible for designing, building, and testing machine learning models for applications such as spacecraft autonomy, anomaly detection, natural language processing, and signal characterization.
The role involves applying ML principles, leading development of AI solutions, researching emerging technologies, and working with cross-functional teams.
The engineer will also contribute to strategic roadmaps, interpretability, scalability, and efficiency of models, and participate in technical interviews.
The position is primarily on-premises at the Westminster office and involves staying up-to-date with research, reading technical papers, and applying knowledge from publications.

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B.S. in Computer Science, Machine Learning, Software Engineering, Aerospace Engineering, or a related 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.
Basic understanding of aerospace engineering.
Strong understanding of algorithms, data structures, and system design principles.
Specialization in certain areas and contribution to process details.
Strong understanding of neural networks (feedforward, recurrence, convolution, attention).
Ability to understand and implement new methods.
Familiarity with reinforcement learning, supervised/unsupervised learning, PCA, clustering, regression, classification.
Understanding of system design and architecture, and ability to apply methods from other domains.
Confidence in writing neural network models from scratch.
Effective use of tools to track model training.
Understanding train/test/validation splits.
Awareness of model evaluation pitfalls.
Proficiency with PyTorch, TensorFlow, Jax, or equivalent.

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