Senior Staff Audio Machine Learning Engineer

Boston Dynamics•Waltham, MA

4d•$180,000 - $220,000

About The Position

Boston Dynamics is seeking a Senior Staff Audio Machine Learning Engineer to join the Spot Sensing team. The role focuses on evolving the Acoustic Change Detection (ACD) capability by transforming beamformed and raw audio into actionable machinery prognostics. The engineer will also contribute to multimodal mechanical inspection, integrating acoustic features with image, video, and thermal data and fusing multimodal representations with LLMs/VLMs to provide users with prescriptive maintenance actions. The work will use fleet-gathered industrial sensor data collected by Spot equipped with the Sorama L642 acoustic camera and SpotCAM2. The position owns acoustic machine learning and digital signal processing pipelines, advances anomaly detection and fault classification, and develops multimodal systems for diagnosing machinery faults and recommending corrective actions. It also involves benchmarking, optimizing, deploying, monitoring, and continuously improving solutions for industrial customers.

Requirements

At least 7 years of practical expertise in audio feature extraction, audio fingerprinting, segmentation, and machine learning applied to beamformed and raw audio for anomaly detection.
Advanced proficiency in Python and C++.
Mastery of numerical and scientific tools such as NumPy, SciPy, scikit-learn, and PyTorch.
Expertise in time-domain and frequency-domain data analysis.
Expertise in multi-channel array processing, including beamforming methods such as Delay-and-Sum, MVDR, and null-steering; Direction of Arrival estimation; and synthetic aperture techniques.
Expertise in unsupervised and supervised classification architectures for anomaly detection in real-world conditions.
Must be eligible to work in the United States; visa sponsorship is not available.

Nice To Haves

Experience in cross-modal fusion, combining audio representations with computer vision, thermal imaging, or multimodal foundation models/VLMs.
Background in rotating machinery health monitoring, including bearings, gearboxes, and pumps, and familiarity with mechanical vibration/acoustic failure standards.
Familiarity with TypeScript or modern web services for integrating work into Orbit fleet management software.

Responsibilities

Design, refine, and scale digital signal processing and machine learning pipelines for the Acoustic Change Detection system.
Improve and modernize the acoustic detection stack, including transitioning from unsupervised models such as BGMM to state-of-the-art anomaly detection architectures.
Architect end-to-end supervised multimodal pipelines for automated fault diagnosis.
Partner with the Spot Apps organization to integrate acoustic insights with visual, thermal, and VLM/LLM data streams into unified machine health indicators.
Deliver clear mechanical diagnoses and recommended corrective actions to plant operators.
Benchmark, optimize, ship, monitor, and continuously improve solutions for industrial customers.