Principal Research Architect

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

About The Position

Bose is seeking a hands-on research architect to design, build, and evaluate experimental hardware and software systems. Working with a multidisciplinary research team, this role compares architectural approaches, integrates emerging technologies, and uses measured results to guide technical decisions. The position focuses on connecting devices, writing code, and investigating system behavior, turning open questions into repeatable experiments and reusable learning with researchers and prototyping engineers. The role integrates models and algorithms developed by other Research teams. It applies knowledge of machine-learning architectures and processing pipelines to evaluate system implications and provide feedback to model developers. The primary focus is system architecture and integration; model-development experience is valuable background. The position also mentors engineers while remaining actively involved in implementation.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Systems Engineering, Robotics, or a related technical discipline.
10+ years of experience designing, architecting, and delivering complex software or systems solutions.
Proven experience architecting connected-device, embedded, distributed, IoT, robotics, or edge-computing systems, including hands-on integration and evaluation of ML models and an understanding of how model architecture affects system behavior.
Hands-on software development and prototyping experience, with the ability to build, debug, and evaluate experimental systems.
Practical experience developing software on Linux-based and/or embedded platforms.
Deep understanding of networking, APIs, messaging systems, event-driven architectures, and software integration patterns.
Experience debugging and measuring behavior across hardware, software, and communication boundaries.
Practical understanding of ML processing pipelines, including input preparation, representations, and inference, sufficient to discuss trade-offs with model developers.
Demonstrated ability to compare implementations, interpret measurements, and revise architectural decisions based on evidence.
Strong technical communication and collaboration skills, with curiosity and initiative as requirements evolve through experiments.
Experience designing or integrating audio or multimedia systems, with an understanding of streaming pipelines, timing, synchronization, and latency.

Nice To Haves

Experience with Python or comparable tools used in ML and systems research.
Master’s degree or higher in a related engineering or computer science discipline.
Prior experience developing or training ML models, providing practical insight into model-design trade-offs.
Experience deploying or optimizing inference on resource-constrained hardware.
Experience with MQTT, ROS 2, Bluetooth/BLE, device discovery, or comparable communication frameworks.
Background in robotics, connected products, wearables, automotive systems, industrial automation, sensing, or audio.
Experience experimenting with agentic development tools or building agent workflows, with an understanding of tool integration, execution boundaries, testing, and human oversight.
Experience guiding research or advanced-development work and turning experimental findings into reusable engineering approaches.

Responsibilities

Design, implement, and compare software and system architectures through working prototypes and repeatable experiments.
Translate research questions into experiment specifications, baseline comparisons, measurable evaluation criteria, and failure tests.
Write and debug critical software components, working directly with devices, sensors, and compute platforms.
Define and test APIs, data models, messaging approaches, and interfaces across hardware, models, and software services.
Collaborate with Research teams to integrate ML models and signal-processing capabilities into working systems.
Evaluate how model inputs, representations, and inference requirements affect quality, latency, memory, compute, power, and reliability; provide actionable feedback to model developers.
Compare processing placement, communication, and coordination approaches, including behavior when connectivity or components fail.
Define synchronization, instrumentation, and reproducibility requirements with the engineers implementing experiments.
Evaluate third-party technologies against internal or open alternatives, considering technical fit, integration effort, portability, and maintenance.
Work with security, privacy, and platform specialists on trust boundaries, data access, and appropriate handling of sensor data.
Document evidence, architectural decisions, and unresolved questions.
Mentor engineers while remaining actively involved in implementation.