Machine Learning Engineer, Senior

Benefits

Parental leave

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Work mode

Onsite From the posting source checked Jun 20, 2026

In-office days

5 days From the posting source checked Jun 20, 2026

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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State eligibility is not yet verified.

About this role

Machine Learning Engineer, Senior Austin | OnSite Location: Onsite - Austin, TX Employment Type: Direct Hire, Full‑Time Job Title: Senior ML Engineer Company Overview 9 Mothers Defense develops AI-enabled systems to counter unmanned aerial threats. Our first product, EDDA, is an autonomous counter-sUAS point-defense platform designed to detect, track, and neutralize Group 1 drone threats. The company is headquartered in Austin, Texas. Position Summary 9 Mothers is seeking a Machine Learning Engineer to design, train, and maintain the models that power our counter-sUAS perception stack. The Machine Learning Engineer is responsible for model research, dataset engineering, and the training infrastructure that supports detection, classification, and discrimination of aerial targets. This is an individual contributor position. We don't use RAG, LLMs, or pre-built cloud APIs. Our stack requires building from the ground up to solve high-stakes problems under strict Size, Weight, and Power (SWaP) constraints. You should be capable of building models from scratch and have a fundamental understanding of the problem space. Essential Duties - Design, train, and iterate on machine learning models for detection, classification, and tracking of aerial targets. - Own the dataset pipeline end-to-end, including data collection, labeling, curation, augmentation, synthetic data generation, and closed-loop retraining based on field performance. - Build and maintain training infrastructure, including experiment tracking, compute orchestration, and evaluation harnesses. - Define metrics and evaluation methodologies that correlate to real-world operational performance. - Support deployment of trained models into the production perception stack, and address discrepancies between training and deployed performance. - Analyze

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