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9 Mothers · 1 day ago

Applied Machine Learning Engineer (Acoustic)

Austin, TX

Full-time

Onsite

Senior Level

$150K/yr - $250K/yr

9 Mothers is a funded startup building autonomous machines for defense markets. They are seeking a Senior Applied Acoustic ML Engineer to use machine learning for reliably detecting, classifying, and tracking acoustic targets, bridging traditional DSP and modern ML.

Defense & Space

No H1B

U.S. Citizen Only

Responsibilities

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

Qualification

Machine Learning for audioPythonDSP fundamentalsRustOn-device inference optimizationEngineering RigorWeak/self-supervised learningFusion/tracking experiencePassion for building robotsCross-Functional Collaboration

Required

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