Develop machine learning models to enhance detection and classification of acoustic targets in outdoor environments, bridging traditional DSP and modern ML methodologies.
Location: Onsite — Austin, TX
Employment Type: Full-Time
Job Title: Senior Applied Acoustic ML Engineer
Company Overview
We are a funded startup building autonomous machines for defense markets. Our first product is designed to counter small, fast FPV suicide drones. Our robots require world-class perception and decision-making. We turn cutting-edge machine learning into field-ready capability.
Position Summary
We are seeking a Senior Applied Acoustic ML Engineer to use machine learning to make our systems reliably detect, classify, and track acoustic targets. You will bridge the gap between traditional DSP and modern ML, ensuring our interceptors can identify threats under extreme domain shift and outdoor noise.
Essential Duties
Build Hybrid Approaches: Develop models that combine beamformed channels and multichannel features for robust detection and classification.
Own the Data Loop: Define labeling strategies, build training/eval pipelines, and implement hard-negative mining to handle diverse outdoor conditions.
Ensure Robustness: Directly reduce false alarms caused by wind, rain, and reflections across different terrains and sensor units.
Ship Edge Inference: Deploy models to edge runtimes with strict latency constraints, integrating diagnostics so the system is operable in real time.
Cross-Functional Collaboration: Work closely with Hardware and DSP teams to align data, calibration, and performance metrics.
Requirements
Experience: You have shipped ML for audio (or similar noisy sensors) into real usage with measurable operational metrics (precision/recall, false alarms).
Engineering Rigor: Disciplined approach to ML engineering, including reproducible experiments, deep ablations, and systematic error analysis.
Foundations: Practical understanding of mic-array fundamentals (SNR, aliasing, sync) enough to debug failures and design robust tests.
Programming: High proficiency in Python; experience with Rust for runtime integration is a significant plus.
Compliance: This position requires access to export-controlled information under ITAR. Only U.S. persons are permitted to access such information.
Background: Must be willing to submit to a background check.
Nice-to-Have
On-device inference optimization (TensorRT, ONNX, quantization)
Weak/self-supervised learning and domain adaptation
Fusion/tracking experience (temporal models, confidence calibration)
Passion for building robots as a hobby
Benefits
Competitive salary + early equity
Opportunity to build systems the Department of Defense actively needs
New lab equipped with Jetsons, scopes, and 3‑D printers
Direct influence on product and technology roadmap
100% employer paid health, dental, and vision insurance
50% employer paid health, dental, and vision insurance for dependents
Unlimited PTO
Relocation assistance
About the interview
Application screen phone call (30 min)
Virtual interview with co-founder #1 (30 min via MS Teams)
Paid take-home ($500, ~4-8 hours)
Review work
Virtual interview with co-founder #2 (45 min via MS Teams)
In-person visit to Austin/Offer
Health Insurance
50% employer paid health, dental, and vision insurance for dependents
Other Benefit
Relocation assistance
9 Mothers is a startup developing autonomous machines tailored for defense applications, with a focus on countering fast and agile FPV suicide drones. We merge advanced perception and decision-making technologies to create field-ready solutions, catering to the evolving needs of modern defense.
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