Approach Venture · 4 hours ago
Acoustic Machine Learning Engineer
Austin, TX
Full-time
Onsite
Senior Level
$140K/yr - $220K/yr
5+ years expApproach Venture is an early-stage, well-funded defense technology startup focused on building autonomous systems for detecting and responding to aerial threats. They are seeking a Senior Applied Acoustic Machine Learning Engineer to lead the development of audio-based detection and classification systems, transforming raw acoustic data into high-confidence real-world decisions.
Management Consulting
Hiring Manager
Torey Bell
Responsibilities
Design and train machine learning models for acoustic detection, classification, and target tracking in outdoor environments
Develop hybrid approaches combining multichannel audio features, beamforming outputs, and modern neural architectures
Build and maintain data pipelines including labeling strategies, evaluation frameworks, and continuous model improvement loops
Implement robustness strategies to reduce false positives caused by wind, weather, reflections, and environmental variability
Deploy optimized inference pipelines for edge hardware with strict latency and performance requirements
Integrate diagnostics and monitoring to support real-time operational performance
Partner with hardware engineers and signal processing specialists to align sensor calibration, data quality, and system metrics
Perform deep error analysis and iterative model refinement to drive real-world reliability
Qualification
Required
5+ years of applied machine learning experience working with audio, sensor data, or similarly noisy real-world signals
Demonstrated experience deploying ML models into real operational environments with measurable performance outcomes
Strong proficiency in Python and modern ML frameworks
Solid understanding of signal fundamentals such as noise sources, synchronization, and sensor behavior
Experience designing reproducible experiments, evaluation pipelines, and systematic model improvement processes
Ability to own projects end-to-end from data through deployment
Preferred
Edge inference optimization including quantization, runtime acceleration, or hardware-aware deployment
Experience with weakly supervised learning, domain adaptation, or robustness techniques
Work on multi-sensor fusion or temporal modeling systems
Familiarity with low-latency embedded or real-time systems
Passion for robotics, autonomy, or hands-on technical projects outside of work
Benefits
Early-stage equity
Company
Approach Venture
Enabling founding teams building the future of frontier technology to achieve their full potential.
2-10 employees
Funding
Current Stage
Early StageLeadership Team
Bryce Dabbs
Founder & CEO
Tristan Cembrinski
Partner
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