Senior Research Prototyping Engineer

Bose Corporation, U.S.A•Framingham, MA

About The Position

Bose is seeking a hands-on engineer to build and evaluate experimental systems that combine devices, software, and research-developed algorithms. Working with a multidisciplinary research team, the engineer will integrate hardware, machine-learning models, and software runtimes; troubleshoot issues across system boundaries; and make experiments repeatable and measurable. The role involves collaborating with researchers and a research architect to implement alternative approaches and measure their behavior. Research teams will supply many of the models and algorithms; the engineer will connect them to inputs, inference runtimes, and applications, diagnose integration issues, and report measured results. Experience creating or training models is helpful but not required.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or a related technical discipline.
8+ years of software engineering, prototyping, embedded systems, IoT, robotics, or connected-device development experience.
Strong software development skills in Python and experience with C or C++.
Experience integrating hardware and software in Linux-based environments, including development boards, sensors, embedded devices, or hardware bring-up.
Hands-on experience integrating and running ML models within connected-device, embedded, robotics, or edge-computing systems, connecting model inputs and outputs to a working application or device pipeline.
Practical understanding of preprocessing, model inference, and runtime dependencies, with the ability to diagnose issues across models, applications, and hardware.
Practical knowledge of networking, device communications, APIs, and distributed system concepts.
Experience configuring and using AI agents for development or engineering workflows, including connecting tools, providing project context, running multi-step tasks, and reviewing and testing results. Professional or substantial personal projects are relevant.
Experience measuring system behavior and developing automated tests, validation tools, and troubleshooting methods.
Disciplined use of version control, configuration management, documentation, and reproducible development practices.
Strong problem-solving and collaboration skills, with initiative to build, investigate, and iterate as experiments evolve.
Hands-on experience integrating audio or multimedia devices and software, including capture, playback, streaming, and debugging timing or synchronization issues.

Nice To Haves

Experience with inference frameworks, model conversion, hardware acceleration, or deployment on resource-constrained platforms.
Experience adapting, optimizing, or developing ML models; model training experience is helpful but not required.
Experience with MQTT, Bluetooth/BLE, ROS 2, device discovery, or comparable communication frameworks.
Background in robotics, connected products, wearables, audio, automotive systems, or industrial automation.
Familiarity with Docker, CI/CD, hardware-in-the-loop testing, diagnostics, or observability tools.
Understanding of secure device integration and data privacy practices.
Master’s degree in a related engineering or computer science discipline.

Responsibilities

Build and maintain prototype systems using connected devices, sensors, audio endpoints, and compute platforms.
Develop software adapters, gateways, and interfaces across IP networks, Bluetooth/BLE, USB, serial, and other device protocols.
Integrate Research-developed and third-party ML models into inference runtimes, connecting sensor inputs, preprocessing, model execution, and outputs to working applications.
Work with model developers to understand interfaces, dependencies, and operating limits; diagnose integration problems and provide measured feedback.
Implement alternative services, messaging, coordination, and runtime approaches using agreed interfaces and comparable test conditions.
Measure latency, throughput, synchronization, memory, compute use, reliability, and recovery; compare runtime configurations with the research architect.
Build automated tests, scenario replay, and fault injection to make experimental results repeatable.
Develop diagnostic tools and displays that make system behavior and experiment outcomes inspectable.
Maintain versioned models, dependencies, configurations, device images, provisioning workflows, and build documentation.
Configure and use AI agents to support development, testing, and engineering tasks, and review and verify their code, actions, and outputs.
Implement approved data-handling, filtering, redaction, and privacy practices with security and platform counterparts.
Troubleshoot across hardware, software, operating systems, networks, and device communication layers.