AI/ML Engineer, SIGINT & ELINT

About the Role

Auric AI Labs is developing an AI-native intelligence system from scratch for defense applications, focused on an AI-powered vision intelligence platform. This role involves building deep learning models that detect, classify, geolocate, and fingerprint radar and communications emitters directly from raw IQ data, aiming for significant improvements over traditional methods. The model itself is the product, not an add-on feature.

Key Responsibilities

• Build deep learning models to detect, classify, geolocate, and fingerprint radar and communications emitters from raw IQ data.
• Design the synthetic data pipeline, channel models, validation methodology, and tests for trustworthy simulation in the real world.
• Address challenges such as signals designed to defeat detection (e.g., LPI radar, frequency-agile emitters), lack of real training data, and the need for fine-grained, unlabelled, open-set identification of unique emitters.
• Engage with DSP methodologies to understand and overcome limitations.

Requirements & Tech Stack

• AI-native instinct: Prioritizing learned functions over pre-defined filters, transforms, or heuristics.
• Thinking in terms of data, loss, and architecture.
• Ability to reason about model-data fit and the suitability of architectures for data structures.
• Understanding of challenges posed by complex-valued signals, long-range temporal dependencies, and small distribution shifts.
• Experimental design: Creating experiments that can fail and building falsifying tests before model development.
• Ability to read research papers critically and identify subtle details or omissions.
• Demonstrated initiative through self-assigned work, side projects, or sustained personal exploration of ideas.
• Skill in translating vague objectives into defensible ML problems by defining inputs, outputs, loss functions, and meaningful evaluation metrics.

Qualifications

• Experience with deep learning for signal processing or related domains is implied by the responsibilities.
• Ability to translate plain English objectives into concrete ML problem definitions.