Jobs at Bose

Research and develop AI-powered audio source separation and extraction algorithms for speech and real-world sounds. Train and evaluate models for single- and multi-microphone recordings, challenging acoustic conditions, perceptual quality, and real-time performance. Collaborate with DSP and embedded software teams to deploy algorithms on physical devices, while contributing to demonstrations, patents, and research publications.

Research and develop real-time AI algorithms for audio source separation and target sound extraction. Train and evaluate models for speech and environmental sounds across challenging acoustic conditions, optimizing perceptual quality, latency, memory, and computational efficiency. Collaborate with DSP and embedded software teams to deploy models on physical devices, while contributing to prototypes, patents, and conference publications.

Research and develop AI-powered audio source separation and extraction algorithms for speech and real-world sounds. Train and evaluate PyTorch models using microphone arrays, spatial cues, and auxiliary sensors; address noise, reverberation, overlapping speakers, latency, memory, and computational constraints. Collaborate with DSP and embedded software teams to deploy real-time models on physical devices, and contribute to patents and research publications.

Research and develop real-time AI algorithms for separating and extracting speech and other sound sources from microphone recordings. Build and evaluate neural audio models under challenging acoustic conditions, optimize perceptual quality and computational efficiency, and collaborate with DSP and embedded software teams to deploy models on physical devices. Contribute to product demonstrations, patents, and research publications.

Research and develop real-time AI algorithms for separating and extracting speech and other sound sources from microphone recordings. The role covers single- and multi-microphone systems, spatial and sensor-conditioned processing, robustness to noise and reverberation, perceptual evaluation, and low-latency deployment on embedded devices. Collaboration with ML, DSP, and embedded software teams is required, along with contributions to product demonstrations, patents, and research publications.

Research and develop real-time AI algorithms for separating and extracting speech and other sound sources from microphone recordings. Train and evaluate audio models under noise, reverberation, overlapping talkers, and motion conditions. Collaborate with DSP and embedded software teams to deploy models on physical devices, balancing perceptual quality, latency, memory, and computational requirements. Contribute to patents, demonstrations, and conference publications.

Research and develop real-time AI algorithms for audio source separation, target sound extraction, and speech enhancement. Work with single- and multi-microphone recordings, spatial cues, auxiliary sensors, and challenging acoustic conditions. Train and evaluate PyTorch models while assessing perceptual quality, latency, memory, and computational efficiency. Collaborate with DSP and embedded software teams to deploy models on physical devices and contribute to patents and research publications.

Research and develop real-time AI algorithms for audio source separation, target sound extraction, and speech enhancement. Train and evaluate PyTorch models using single- and multi-microphone recordings, spatial cues, and auxiliary sensors. Address overlapping speakers, noise, reverberation, motion, perceptual quality, latency, memory, and computational constraints. Collaborate with DSP and embedded software teams to deploy models on physical devices, while contributing to patents, publications, and product demonstrations.

Lead creation of an Automotive regional experience team for China: define region-specific software platform and product roadmaps, align with sales and engineering, own regional product financials, scout and negotiate partnerships, manage partner relationships and cross-functional launches, and ensure platform capabilities meet OEM and market needs.

Research and develop real-time AI algorithms for isolating, extracting, and suppressing sound sources from microphone recordings. Responsibilities include training and evaluating audio models, addressing noise and reverberation, optimizing perceptual quality and computational efficiency, and collaborating with DSP and embedded software teams to deploy models on physical devices. The role also supports patents, conference publications, and novel audio experiences.

Research and develop real-time AI algorithms for audio source separation, target sound extraction, and speech enhancement. Train and evaluate models for challenging acoustic conditions, including overlapping speakers, noise, reverberation, and motion. Collaborate with DSP and embedded software teams to deploy efficient models on physical devices, balancing perceptual quality, latency, memory, and computational requirements. Contribute to product demonstrations, patents, and conference publications.

Leads Bose’s global visual identity and brand expression across product launches, campaigns, photography, video, experiences, partnerships, packaging, and physical environments. Defines creative systems, directs external partners, translates product innovation into emotionally compelling storytelling, and ensures consistent brand execution. Builds and mentors multidisciplinary creative teams while collaborating with Brand, Product, Partnerships, Experiential, Communications, and Marketing leaders.

Designs, builds, and evaluates experimental hardware and software architectures for connected, embedded, distributed, audio, and ML-enabled systems. Responsibilities include prototyping, integration, API and messaging design, performance measurement, failure testing, device and sensor debugging, security and privacy collaboration, architectural evaluation, documentation, and mentoring engineers.

Six-month research co-op developing and evaluating multimodal sensor fusion, DSP, and machine learning algorithms for room environmental sensing. Responsibilities include collecting microphone, IMU, camera, and light-based sensor data; developing models, training datasets, and evaluation infrastructure; and implementing real-time prototypes. The role requires Python, MATLAB, TensorFlow or PyTorch, signal processing, deep learning, acoustic modeling, and real-time processing knowledge.

Machine Learning/DSP Research Engineer Intern developing and evaluating multimodal sensor fusion, digital signal processing, and machine learning algorithms for room environmental sensing. Responsibilities include collecting microphone, IMU, camera, and light-based sensor data; building machine learning models and training datasets; and developing evaluation infrastructure for real-time applications.

Build and evaluate experimental systems integrating connected devices, sensors, audio endpoints, software, and machine-learning models. Develop protocol adapters, inference pipelines, automated tests, diagnostics, and reproducible experimentation workflows. Measure system performance, troubleshoot across hardware and software boundaries, manage dependencies and device configurations, and collaborate with research teams to compare alternative runtime and system approaches.

Develop agentic workflows and reliable automation for real-time audio machine learning research. Build systems that prepare data, train and evaluate models, run hyperparameter searches, perform regression testing, manage experiments and artifacts, and generate reports. Operate on-premises and cloud GPU infrastructure, connect LLM agents to research tools and resources, and collaborate with audio researchers to create reproducible workflows, dashboards, and observability systems.

Develop agentic workflows and automation for real-time audio machine learning research. Responsibilities include orchestrating data preparation, training, evaluation, hyperparameter searches, benchmarking, regression testing, experiment tracking, GPU scheduling, artifact management, and reproducible execution. The role connects LLM agents with research tools and compute resources, builds dashboards and reports, and partners with audio researchers to create reliable, observable ML systems.

Develop agentic workflows and automation for real-time audio machine learning research. Responsibilities include orchestrating data preparation, training, evaluation, hyperparameter searches, benchmarking, regression testing, experiment tracking, artifact management, GPU scheduling, and reproducible execution. The role connects LLM agents with research tools and infrastructure, builds dashboards and reports, and partners with audio researchers. Requires strong Python and software engineering skills, ML pipeline experience, PyTorch, Linux GPU operations, CUDA, containers, and audio ML/DSP knowledge.

Build agentic workflows and automation for real-time audio machine-learning research. Responsibilities include orchestrating data preparation, training, evaluation, hyperparameter searches, benchmarking, regression testing, experiment tracking, artifact management, and reporting. The role connects LLM agents with research tools, datasets, repositories, evaluation suites, and GPU infrastructure while improving reproducibility, observability, and efficiency. Candidates collaborate with audio researchers and work with Python, PyTorch, Linux GPU systems, CUDA, containers, and audio ML/DSP workflows.