Senior Manager, Knowledge Engineering & AI Agent Enablement

$138K - $172K Boston, MA, US Senior AI Agent Developer

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Skills & Technologies

BedrockSalesforce

About This Role

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At Sonos we want to create the ultimate listening experience for our customers and know that it starts by listening to each other. As part of the Sonos team, you’ll collaborate with people of all styles, skill sets, and backgrounds to realize our vision while fostering a community where everyone feels included and empowered to do the best work of their lives.

This is an on site position. Qualified applicants must live within commuting distance of our Santa Barbara, Seattle, or Boston office locations and should expect to be in office 5 days a week.

Visa Sponsorship: Sonos is unable to sponsor or take over sponsorship of an employment visa for this role at this time. We ask that applicants be authorized to work for us in country of role posting, both now and in the future.

Sonos is a leading technology hardware and software company dedicated to delivering innovative home audio experiences. Our Care organization powers customer success through deep technical expertise, and Knowledge and AI are our strategic advantage. We are building a modern, AI-enabled knowledge ecosystem and autonomous service agents that together accelerate resolution, empower agents and customers, and drive continuous improvement across our global support operations.

We are hiring a Senior Manager, Knowledge Engineering & AI Agent Enablement to serve as product owner for two connected capabilities at the core of Sonos Care's AI transformation: the structured, machine-ready knowledge that autonomous agents and retrieval systems depend on, and the autonomous service agents themselves. This is a dual-remit people leadership role reporting to the Director, CX Product within the Care Enablement & Services organization.

What You'll Do

  • Own the product vision, roadmap, and backlog for Knowledge Engineering and AI Agent Enablement as a unified capability, translating business and customer needs into clear, prioritized requirements for engineering, data science, and AI partner teams.
  • Lead the Knowledge Engineering team and the AI Agent Enablement team: hire, coach, and develop managers and individual contributors across both disciplines, set goals, calibrate performance, and build career paths in two fast-evolving fields.
  • Direct the strategy for making structured knowledge assets clean, well-modeled, and AI-ready, including taxonomy and ontology governance, metadata standards, data quality, and the retrieval layers (search, retrieval-augmented generation) that humans and agents draw on.
  • Own the roadmap for autonomous service agents — which issues they resolve, how they escalate, and how they improve over time — overseeing conversation and prompt design, tool and API integrations, and grounding agents in structured knowledge systems.
  • Establish agent evaluation and benchmarking practices covering accuracy, containment rate, safety, and quality, with human review loops and clear escalation paths to human agents.
  • Govern agent behavior and risk: guardrails, hallucination controls, brand and tone consistency, and compliance sign-off before agents go live or expand scope.
  • Run agile planning, backlog refinement, sprint reviews, and retrospectives across both teams, partnering with support engineering, data science, and external AI partners on underlying orchestration and infrastructure.
  • Define, track, and report on KPIs across both remits — knowledge usage, content quality, agent containment, resolution accuracy, escalation rate, customer satisfaction, and combined effects on handle time and case deflection — and use data to drive continuous improvement.
  • Partner with Support leadership, Diagnostic Engineering, Data Science, Training & Quality, and executive stakeholders to prioritize investment, close capability gaps, and present strategy and business impact to senior leadership.
  • Establish governance frameworks, content and agent-behavior standards, lifecycle processes, and compliance requirements (accessibility, legal, brand, safety) across both functions.

What You'll Need

Basic Qualifications

  • Bachelor's degree in Information Science, Computer Science, Business, or a related field.
  • 8+ years of progressive experience in knowledge management, AI or automation product management, or
  • 3+ years of people leadership experience, ideally leading more than one function or discipline.
  • Demonstrated experience as a product owner or in a delivery-focused product role, including backlog
  • Hands-on or leadership experience with autonomous or conversational AI agents in a customer support or service context, including agent design, orchestration, evaluation, or governance.
  • Strong background in knowledge engineering fundamentals: taxonomies, metadata, information architecture, and knowledge representation — or demonstrated success leading engineers who work in these areas.
  • Understanding of how machine learning pipelines consume structured knowledge, including retrieval-augmented generation knowledge bases and how agent quality depends on knowledge quality.
  • Familiarity with KCS (Knowledge-Centered Service) methodology and its application in technical support Excellent communication, stakeholder management, and executive presentation skills, including the ability to translate technical and semantic decisions for non-technical audiences.
  • Strong analytical, program management, and change management capabilities.
  • Qualified applicants must be able to work from the office 5 days per week.

Preferred Qualifications

  • Master's degree in a relevant field.
  • Certifications in knowledge management (e.g., Certified Knowledge Manager), KCS, or AI/ML product disciplines.
  • Experience in large-scale enterprise technology companies with high-volume technical support operations.
  • Experience standing up or scaling an AI agent evaluation and benchmarking practice covering accuracy, safety, and containment.
  • Familiarity with semantic web standards (SKOS, OWL, schema.org) and modern knowledge management or AI platforms such as Salesforce Service Cloud, Confluence, or vector and semantic search tools.
  • Experience with agent orchestration platforms such as Salesforce Agentforce or Amazon Bedrock, or comparable tooling.
  • Global or distributed team leadership experience and a track record of driving adoption of new processes and tools across multiple teams.
  • Thought leadership contributions — writing, speaking, or community involvement — in knowledge management, KCS, or AI-enabled support practices.

Inclusion: Research shows that some candidates may not apply for roles if they don't meet all the criteria. If you don't have 100% of the skills listed, we still strongly encourage you to apply if interested.

Your profile will be reviewed and you'll hear from us once we have an update. At Sonos we take the time to hire right and appreciate your patience.

The base pay range for this role based off geographic location is:

$138,000 and $172,100 The specific pay offered will depend on the candidate’s geographic location, as well as qualifications and experience. We apply geographic pay differentials based on the cost of labor in the market. Employees in high-cost locations may be compensated at the upper end of the range, while those in medium or low cost markets may be compensated at the lower end of the range. Your recruiter can provide more details about the specific salary range for your location during the hiring process.

Please note that compensation details listed in US job postings reflect the base salary only, and do not include bonus, equity, or benefits.

We also offer a comprehensive benefits program with choice and flexibility in mind to help support the health, wealth, and overall well-being of our employees. Regular full time employees in the US are eligible for benefits on day one, including:

  • Medical, Dental, and Vision Insurance
  • A 401(k) plan with company matching and immediate vesting
  • An Open Time Off policy (OTO) so you have maximum opportunity to disconnect and recharge, with no tenure-based vacation accruals required
  • 80 hours of sick time upon hire, refreshed annually
  • Up to 12 paid holidays per calendar year
  • Sonos offers a generous paid leave program for new parents or to care for a family member with a serious health condition, as well as short- and long-term disability for your own medical condition
  • Company-paid Disability, Life, and AD&D Insurance
  • Voluntary benefits, including Voluntary Life, AD&D, Accident, and Pet Insurance
  • Mental health benefits to support your holistic well-being
  • A generous employee discount program & Sonos Radio HD - on us!

Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law.

Notice to U.S. Job Applicants: Sonos is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other legally protected characteristics.

Sonos is committed to working with and providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the employment process, please send an e-mail to [email protected] and let us know the nature of your request and your contact information.

Salary Context

This $138K-$172K range is below the median for AI Agent Developer roles in our dataset (median: $175K across 28 roles with salary data).

View full AI Agent Developer salary data →

Role Details

Company Sonos
Title Senior Manager, Knowledge Engineering & AI Agent Enablement
Location Boston, MA, US
Experience Senior
Salary $138K - $172K
Remote No

About This Role

AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.

Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.

Across the 9,079 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Sonos, this role fits into their broader AI and engineering organization.

AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.

What the Work Looks Like

A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.

AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.

Skills Required

Bedrock (2% of roles) Salesforce (1% of roles)

Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.

The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?

Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.

Compensation Benchmarks

AI Agent Developer roles pay a median of $215,000 based on 203 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($155K) sits 28% below the category median. Disclosed range: $138K to $172K.

Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.

Sonos AI Hiring

Sonos has 1 open AI role right now. They're hiring across AI Agent Developer. Based in Boston, MA, US. Compensation range: $172K - $172K.

Location Context

AI roles in Boston pay a median of $215,000 across 360 tracked positions.

Career Path

Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.

From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.

Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.

What to Expect in Interviews

Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.

When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.

AI Hiring Overview

The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.

The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).

AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.

The AI Job Market Today

The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.

The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.

AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.

Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.

The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.

Frequently Asked Questions

Based on 203 roles with disclosed compensation, the median salary for AI Agent Developer positions is $215,000. Actual compensation varies by seniority, location, and company stage.
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
About 6% of the 9,079 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Sonos is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI Agent Developer positions include AI Architect, Principal Engineer, Head of AI Engineering. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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