New opportunity

Systems Architect, Edge AI

Bose Corporation · Framingham, United States of America

About this role

Bose Labs seeks a Systems Architect, Edge AI to turn emerging audio, sensing, machine learning, and AI technologies into reusable architectures, platforms, reference designs, and intellectual property. The role spans low-power wearables, phones, PCs, Macs, and Linux-based embedded systems, with a focus on secure, scalable edge-AI solutions.

Responsibilities

Architect complex embedded hardware and software systems; assess technical feasibility, tradeoffs, risk, and scalability of new concepts.
Build simulations, prototypes, and experiments to evaluate candidate architectures. Develop AI agents, agentic workflows, and automated engineering infrastructure for architecture exploration and verification.
Stress-test solutions against power, compute, memory, latency, performance, and security constraints. Design algorithms, models, frameworks, and IP for secure integration into third-party systems.
Help shape the technology roadmap and provide principal-level technical leadership across Bose Labs, engineering teams, suppliers, and external partners.

Required qualifications

Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, Systems Engineering, or a related field, or equivalent practical experience. Significant experience developing or architecting complex embedded hardware and software systems, including systems architecture across both disciplines; no specific number of years is stated.
Experience with embedded processors, SoCs, DSPs, real-time systems, and Linux-based platforms; strong knowledge of digital audio signal processing and audio architectures.
Experience implementing or deploying ML models on embedded or edge devices, understanding of compute, memory, power, latency, and hardware-acceleration constraints, and demonstrated use of AI to solve technical problems with measurable engineering or business impact.

Preferred qualifications include AI agents and AI-enabled engineering systems; ML model development, training, evaluation, or optimization; AI-enabled simulation and verification; TinyML, NPUs, DSP-based acceleration, or heterogeneous computing; audio ML, speech processing, spatial audio, or intelligent noise management. Experience with advanced audio products, hardware/software co-design, secure embedded architectures, SDKs, reference designs, semiconductor vendors, technology partners, third-party integrators, or licensing models is also preferred.

The position is full-time, with 40 hours per week listed. The hiring range for Framingham is $168,300-$231,450; ranges at other Bose locations may vary. Listed rewards include bonus programs, health and welfare benefits, a 401(k) plan, wellbeing perks, and an employee discount.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Systems architectureEmbedded systemsEmbedded processorsSystem-on-chip (SoC)Digital signal processing (DSP)Digital audio signal processingReal-time systemsLinuxMachine learningEdge AIEdge ML deploymentHardware accelerationAI agentsAgentic workflowsSimulationPrototypingDesign-space explorationTechnical verificationTinyMLNeural processing units (NPUs)Heterogeneous computingAudio machine learningSpeech processingSpatial audioHardware/software co-designSecure embedded architecturesSDKsTechnical leadership

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Turn emerging technologies into reusable architectures
The posting calls for translating audio, sensing, ML, and AI concepts into embedded hardware/software architectures, platforms, reference designs, and reusable IP. Prepare to explain how you compare candidate designs, prove feasibility, and decide what can scale across devices.
Make edge-AI tradeoffs measurable
The role stress-tests architectures against power, compute, memory, latency, performance, and security constraints. Prepare an example of an embedded ML or audio architecture decision supported by measurements, including the alternatives and risks considered.
Improve architecture exploration with AI
Building agentic workflows, simulations, prototypes, and automated verification infrastructure is a defining duty; the posting also requires measurable impact from using AI on technical problems. Prepare to describe how you would validate an AI-enabled engineering workflow and measure its value.
Design for secure third-party integration
The role architects algorithms, models, frameworks, and IP for secure integration into third-party systems. Prepare to explain how integration boundaries and security considerations affect a reusable design without assuming a particular partner implementation.
Lead technical decisions across groups
The posting calls for roadmap influence and principal-level leadership across Bose Labs, engineering teams, suppliers, and external partners. Prepare an example of communicating architectural evidence and tradeoffs to groups with different technical concerns.

Likely challenges to think through

Portability versus device-specific optimization
A likely design difficulty is keeping an architecture reusable across low-power wearables, phones, PCs, Macs, and Linux-based embedded systems while exploiting each platform’s compute and acceleration options. Prepare a comparison of what you would standardize, what you would adapt, and how you would test both choices.
Audio quality versus edge constraints
A plausible tradeoff is preserving audio or ML performance while meeting real-time latency, power, memory, and security limits. Prepare a constraint budget and an experiment plan that shows when you would change the model, algorithm, hardware allocation, or architecture.
Faster exploration versus trustworthy evidence
Agentic workflows and automated loops could accelerate design-space exploration, but generated analyses still need verification before informing architecture decisions. Prepare to explain checkpoints, reproducible experiments, and measures that distinguish a useful result from an unverified suggestion.

Practice your answers

Technical practice

How would you architect an embedded audio-and-sensing system that runs edge AI across devices with very different resources?

What to coverThe role spans low-power wearables, phones, PCs, Macs, and Linux-based embedded systems. Suggested preparation: explain how you would separate reusable functions from device-specific hardware and software, then compare power, latency, security, and scalability trade-offs.

How would you design a real-time digital audio processing path alongside an ML workload?

What to coverDigital audio signal processing, audio architectures, DSPs, and real-time systems are stated qualifications. Suggested preparation: discuss scheduling, buffering, end-to-end latency, and how you would detect audio-quality or deadline failures.

How would you determine whether an ML model can run on a constrained edge device?

What to coverThe posting calls for embedded ML deployment under compute, memory, power, and latency constraints. Suggested preparation: describe how you would measure model quality and resource use, compare optimization options, and decide when a candidate is not feasible.

How would you choose among an embedded processor, DSP, NPU, and other compute resources for an edge-AI pipeline?

What to coverEmbedded processors, SoCs, DSPs, and hardware acceleration are relevant to the role; NPUs and heterogeneous computing are preferred experience. Suggested preparation: compare workload fit, data movement, latency, power, and integration complexity rather than assuming one accelerator is always best.

What simulation, prototype, or experiment would you use to test a new system architecture before committing to it?

What to coverThe role includes proving technical feasibility and evaluating candidate architectures through simulations, prototypes, and experiments. Suggested preparation: state the key uncertainty, the measurements and decision criteria, and what result would change your architectural choice.

How would you build an agentic workflow for architecture exploration without trusting its conclusions blindly?

What to coverThe posting explicitly calls for AI agents and automated engineering loops for exploration and verification. Suggested preparation: explain where agents could generate or evaluate alternatives, how checks would verify their outputs, and how you would measure engineering impact.

How would you stress-test an architecture that meets average performance targets but fails under some operating conditions?

What to coverStress-testing power, compute, memory, latency, performance, and security is a stated duty. Suggested preparation: describe how you would reproduce the failure, isolate its hardware or software cause, and compare fixes against the full set of constraints.

How would you design reusable algorithms or models for secure integration into a third-party system?

What to coverThe role includes architecting algorithms, models, frameworks, and IP for secure third-party integration. Suggested preparation: address integration boundaries, protection of sensitive functionality, verification responsibilities, and the trade-off between portability and platform-specific optimization.

What would you put in a reference design so another team could evaluate and reuse an edge-AI architecture?

What to coverBose Labs aims to produce reusable technology building blocks, platforms, and reference designs across products and partners. Suggested preparation: cover defined interfaces, assumptions, performance evidence, constraints, and which elements must be adapted for a new device.

How would you evaluate an audio-ML feature such as speech processing, spatial audio, or intelligent noise management on-device?

What to coverThese audio-ML areas appear as preferred experience, not specified projects for this position. Suggested preparation: choose one as an illustrative case and explain quality measures, representative operating conditions, and trade-offs with latency, power, and audio performance.

Behavioural practice

Tell me about a time you had to make an architecture decision while the problem was still ambiguous.

What to coverThe posting values exploring multiple solutions and using evidence before converging. Suggested preparation: give a real situation, the alternatives and experiments you led, and the result of the decision.

Describe a time you aligned hardware and software teams around a difficult system trade-off.

What to coverThis principal-level role spans both disciplines and provides technical leadership across engineering teams. Suggested preparation: use a real situation/action/result example that shows how you resolved competing constraints and what changed.

Tell me about a technical decision you made with a supplier or external partner.

What to coverThe role involves suppliers and external partners, with third-party integration also part of its scope. Suggested preparation: describe the real situation, how you handled differing technical priorities or boundaries, and the outcome.

Describe a time you used AI to solve a meaningful engineering problem.

What to coverThe posting asks for AI use with measurable engineering or business impact. Suggested preparation: explain the real problem, your actions and validation approach, and the measured result without attributing gains to AI that the evidence cannot support.

Tell me about a time your technical findings influenced a longer-term technology direction.

What to coverInfluencing the technology roadmap is an advertised responsibility. Suggested preparation: give a real situation/action/result account of the evidence you presented, how you communicated risks and alternatives, and what decision followed.

Sharpen your knowledge

Topics to prepare

Cross-disciplinary embedded architecture
The posting requires systems architecture across embedded hardware and software, including processors, SoCs, DSPs, real-time systems, and Linux-based platforms. Revision exercise: sketch two hardware/software partitions for an edge-audio pipeline and explain scheduling, interfaces, and scalability tradeoffs. The sketch is a preparation suggestion, not a specified design task.
Digital audio signal processing
Strong knowledge of digital audio signal processing and audio system architectures is a minimum qualification. Revision exercise: trace an audio signal from capture through processing to output, identifying where latency, buffering, and DSP placement affect the architecture. This example pipeline is suggested practice, not a stated product design.
Edge-ML deployment constraints
The posting requires experience deploying ML on embedded or edge devices and understanding compute, memory, power, latency, and hardware acceleration. Revision exercise: compare two model-placement choices against those constraints and state what measurements would decide between them. The comparison is a suggested way to prepare.
Reuse, scalability, and secure integration
The role turns technologies into reusable architectures and IP, and architects algorithms, models, and frameworks for secure integration into third-party systems. Revision exercise: outline an architecture’s reusable components, integration boundaries, security risks, and scaling limits. That outline is suggested practice, not a prescribed deliverable.
AI-enabled architecture exploration
The role includes agentic workflows, simulations, and automated engineering loops for exploration and verification; agent experience is listed as preferred. Revision exercise: diagram a loop that proposes candidate architectures, tests constraints, records evidence, and flags failures for review. The loop design is a preparation suggestion.

Skills to revise

Prototype and stress-test a candidate design
The posting calls for simulations, prototypes, and experiments that test power, compute, memory, latency, performance, and security. Practice suggestion: build a small simulation or prototype of a hypothetical edge-audio path, vary one constraint, and record the resulting tradeoff and remaining risk. No particular tool is specified.
Measure an edge-ML deployment
Implementing or deploying ML models on embedded or edge devices is a minimum qualification. Practice suggestion: run a suitable model on an available edge platform and document memory use, latency, and power estimates alongside any acceleration assumptions; distinguish measured results from estimates.
Test an audio processing chain
The minimum qualifications emphasize digital audio DSP and audio architectures. Practice suggestion: implement a small audio-processing chain, measure end-to-end latency and processing cost, then explain how a DSP-versus-processor partition could change those results. The chain is an exercise, not a Bose project.
Verify an AI-assisted engineering loop
Developing agentic AI workflows for architecture exploration and verification is a stated responsibility. Practice suggestion: construct a limited workflow that generates design alternatives, checks each against explicit constraints, and produces a reviewable evidence log; identify what still needs human validation.
Present an architecture decision
The role evaluates feasibility, risk, and scalability and provides principal-level leadership across engineering teams, suppliers, and partners. Practice suggestion: prepare a one-page decision record comparing two hypothetical designs, with evidence, unresolved risks, and questions for external integrators. This format is suggested preparation, not a stated company process.

Your preparation checklist

Prepare an embedded-systems project walkthrough
The role requires architecture across hardware and software. If you have a relevant project, prepare a diagram and decision log covering processor or SoC, DSP, real-time behavior, Linux integration, alternatives considered, and your own contribution.
Document an edge-ML deployment with measurements
The posting calls for embedded or edge ML deployment. Prepare available evidence of model performance, compute and memory use, power, latency, and any hardware-acceleration tradeoffs; distinguish measured results from estimates.
Practice an audio architecture design exercise
As preparation for the stated audio-DSP and sensing focus, sketch a hypothetical low-power audio system. Show the signal path, model placement, hardware/software boundaries, and how you would test feasibility and scalability.
Outline an AI-enabled verification workflow
The role includes agents, simulations, and automated architecture exploration. Prepare a workflow diagram showing candidate generation, simulation, checks, human review, and—if you have a real example—measurable engineering impact.
Plan for secure reuse across systems
The posting calls for reusable architectures and secure integration into third-party systems. Prepare a hypothetical reference-design boundary: interfaces, portability assumptions, security risks, and verification needed before reuse.
Prepare a principal-level decision story
The role involves roadmap influence and leadership across engineering teams, suppliers, and partners. Select a real example, if available, that shows how you compared options, handled uncertainty, communicated risk, and reached a technical decision.
Bring targeted questions for Bose Labs
Ask which device environments and constraints are the immediate priorities, how SEA&P evaluates reusable reference designs, and how architecture decisions are coordinated with product teams and external partners.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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