Principal Research Architect

Bose Corporation•Framingham, MA

1d•$168,300 - $231,450

About The Position

Bose is seeking a hands-on research architect to design, build, and evaluate experimental hardware and software systems. Working in a multidisciplinary research team, the architect will compare system approaches, integrate emerging technologies, and use measured results to guide technical decisions. The role suits a curious builder who enjoys connecting devices, writing code, and investigating unexpected behavior, working with researchers and prototyping engineers to turn open questions into repeatable experiments and reusable learning. The architect will primarily focus on system architecture and integration, bringing an understanding of machine-learning architectures and processing pipelines to evaluate system implications and provide feedback to model developers. Models and algorithms may be supplied by other Research teams; model-development experience is useful background. Bose describes itself as a privately owned, customer-focused company with more than 60 years of innovation. It is shaping the future of audio technology, automotive, luxury, and premium experiences. The hiring range for the primary work location in Framingham, Massachusetts, is $168,300–$231,450; ranges at other Bose locations may vary. Compensation includes competitive base pay and may include bonus programs, comprehensive health and welfare benefits, a 401(k) plan, wellbeing perks, and an employee discount.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Systems Engineering, Robotics, or a related technical discipline.
10 or more 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 machine-learning models and understanding 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 machine-learning 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

Master’s degree or higher in a related engineering or computer science discipline.
Prior experience developing or training machine-learning 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.
Experience with Python or comparable tools used in machine-learning and systems research.

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 while 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 machine-learning models and signal-processing capabilities into working systems.
Evaluate how model inputs, representations, and inference requirements affect quality, latency, memory, compute, power, and reliability, and 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 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.