About us
HighLevel is an AI-powered business operating system that gives agencies, entrepreneurs and SMBs the infrastructure to build, automate and scale. Today, HighLevel supports SMBs across 150+ countries, fueling community-driven growth rooted in real customer outcomes.
To date, businesses operating on HighLevel have generated over $7 billion in ecosystem value, demonstrating the impact of shared infrastructure at scale. By centralizing conversations, automation and intelligence into one system, we help businesses move faster, reduce complexity and execute efficiently. Behind the platform, HighLevel powers more than 4 billion API hits and 2.5 billion message events daily. With 250 terabytes of distributed data, 250+ microservices and over 1 million domain names supported, our architecture is built for performance, resilience and long-term scalability.
Our people
With over 2,000 team members across 10+ countries, HighLevel operates as a global, remote-first organization built for speed and ownership. We value initiative, clarity and execution, creating space for ambitious people to build systems that support millions of businesses worldwide. Here, innovation thrives, ideas are celebrated and people come first, no matter where they call home.
Our impact
Every month, HighLevel enables more than 1.5 billion messages, 200 million leads and 20 million conversations for the more than 1 million businesses we support. Behind those numbers are real people building independence, expanding opportunity and creating measurable impact.
We’re proud to be a part of that. Learn more about us on our YouTube Channel or Blog Posts
Role Overview
HighLevel is a powerful platform containing hundreds of interconnected capabilities across CRM, marketing, communications, automation, commerce, websites, calendars, reporting, and AI. That breadth creates enormous value - but it also introduces complexity. Users should not need to know which menu contains a capability, how every object in HighLevel is modelled, or which sequence of screens is required to complete a business task. They should be able to describe what they want to accomplish, receive an accurate answer, and safely complete the work.
Ask AI is the natural-language operating layer designed to make that possible. It allows agencies and businesses to ask questions, understand customer and operational data, generate content and assets, create product configurations, invoke specialised agents, execute supported actions, automate recurring work, and interact with HighLevel through text, voice, structured artefacts, and guided workflows.
For Ask AI to be dependable, it needs accurate and current context. HighLevel Knowledge Bases provide the shared intelligence foundation for Ask AI and the broader AI Employee ecosystem. They ingest and organise information from websites, FAQs, rich text, documents, tables, cloud files, and other approved sources so AI products can retrieve relevant information and produce grounded responses.
We are hiring a Principal Product Manager to own the long-term product strategy for this intelligence and assistance platform. This person will define the architecture, product principles, quality systems, developer model, governance, metrics, and commercial strategy required to make Ask AI the default way users understand and operate HighLevel.
This is not a generic chatbot, enterprise-search, or content-generation role. It is a senior individual-contributor product leadership role at the intersection of AI assistants, enterprise knowledge, retrieval systems, agent orchestration, workflow execution, CRM data, permissions, automation, developer platforms, and multi-tenant SaaS. You will influence multiple product and engineering teams, establish shared platform standards, and make foundational decisions that affect every AI product across HighLevel.
Key Responsibilities
- Define and drive the long-term product vision, strategy, roadmap, and platform architecture for HighLevel Knowledge Bases and Ask AI.
- Own the complete Ask AI experience, including chat, voice interaction, conversation history, memory, templates, artefacts, scheduled tasks, tools, actions, agent routing, browser interaction, feedback, permissions, approvals, auditability, usage, and lifecycle management.
- Own the Knowledge Base platform across source ingestion, extraction, parsing, chunking, embeddings, indexing, metadata, retrieval, re-ranking, citations, freshness, versioning, testing, permissions, and observability.
- Establish the product principles that determine how Ask AI answers questions, requests more information, retrieves knowledge, generates a plan, selects a tool, invokes an agent, asks for approval, executes an action, handles failure, and communicates results.
- Create a shared context model spanning user identity, agency, location, role, permissions, current interface, business profile, Brand Voice, memory, Knowledge Bases, CRM data, product configuration, and previous tool outputs.
- Develop a deep understanding of agency owners, marketers, sales teams, customer-service teams, operations leaders, administrators, and SMB employees who use HighLevel to perform daily work.
- Personally inspect Ask AI conversations, tool traces, Knowledge Base retrieval results, user feedback, support tickets, source-ingestion failures, incorrect answers, failed actions, and downstream customer outcomes.
- Partner closely with AI and engineering leadership on LLM orchestration, model routing, context management, prompt systems, tool invocation, agent planning, memory, retrieval-augmented generation, embeddings, hybrid search, re-ranking, caching, latency, evaluation, observability, and cost.
- Partner with platform and infrastructure teams on ingestion pipelines, crawlers, document processing, vector storage, structured-data retrieval, indexing, refresh jobs, multi-tenant isolation, scalability, reliability, and data retention.
- Define an evaluation architecture covering Ask AI responses, Knowledge Base retrieval, memory usage, tool selection, action accuracy, permissions, artefacts, latency, and cost.
- Build representative test datasets from real agency and SMB use cases across industries, account types, languages, data structures, and product workflows.
- Partner with HighLevel product teams to expose their capabilities safely through Ask AI using shared action, permission, approval, error, and result contracts.
- Establish a scalable onboarding model for product teams that want to contribute Ask AI actions, templates, skills, artefacts, or specialised agents.
- Advance integration between Ask AI and Agent Studio so custom agents can be discovered, selected, invoked, monitored, and improved from a unified Ask AI experience.
- Define the platform model for MCP tools, native product actions, external APIs, web search, browser execution, Knowledge Base retrieval, and other agent capabilities.
- Build robust observability and debugging experiences that show selected context, retrieved sources, model outputs, plans, tools, inputs, permissions, approvals, action results, errors, latency, and usage.
- Improve Knowledge Base quality-management workflows, including retrieval testing, source inspection, stale-content detection, crawl diagnostics, document-processing errors, conflict detection, and customer-facing recommendations.
- Define the architecture for reusable, inherited, bundled, and marketplace-distributed Knowledge Bases across agencies and locations.
- Partner with Design to simplify Knowledge Base creation, source addition, testing, maintenance, agent attachment, Ask AI onboarding, template discovery, approval flows, artefact interaction, and error recovery.
- Develop a coherent experience across Ask AI templates, open-ended prompting, guided questions, native skills, specialised agents, and scheduled tasks.
- Define instrumentation across the full funnel, including Ask AI discovery, first prompt, first useful response, first successful action, repeat usage, template completion, scheduled-task retention, Knowledge Base creation, source ingestion, retrieval tests, agent attachment, and sustained production usage.
- Define quality and outcome metrics by request type rather than treating every Ask AI interaction as equivalent.
- Partner with Security, Legal, Privacy, and Trust teams on data isolation, access control, memory, browser automation, sensitive data, source permissions, content retention, external-model processing, audit logs, and abuse prevention.
- Establish standards for destructive actions, bulk changes, financial operations, external communication, and compliance-sensitive workflows.
- Partner with Product Marketing on positioning, use-case packaging, customer education, competitive differentiation, launches, templates, and agency enablement.
- Partner with Support, Implementation, Trial Experience, Account Management, Affiliates, and internal operations teams to reduce setup friction, improve outcomes, and lower support burden.
- Partner with Finance and Revenue Experience on packaging, pay-per-use pricing, AI Employee plans, agency rebilling, cost controls, gross margin, and commercial limits.
- Develop a clear competitive understanding of AI copilots, enterprise search, AI workspaces, CRM assistants, agent platforms, browser agents, knowledge-management products, and workflow-automation tools.
- Evaluate external model providers, search and retrieval technologies, document-processing systems, vector databases, re-ranking systems, browser technologies, and strategic partnerships.
- Influence strategy across Conversation AI, Voice AI, Agent Studio, Workflow AI, CRM, automation, communications, commerce, and the broader HighLevel platform.
- Act as a senior product thought partner to Product, Engineering, Design, Data, Security, GTM, Finance, and executive leadership.
- Mentor PMs and raise the quality of AI product strategy, platform thinking, evaluation, and decision-making across the organisation without relying on formal authority.
Ideal Candidate Profile
- 12+ years of product management experience owning complex B2B SaaS, AI-assistant, enterprise-search, knowledge-management, workflow-automation, CRM, developer-platform, or AI-agent products. Scope and demonstrated outcomes matter more than a precise number of years.
- Prior experience operating as a Principal PM, Staff PM, Lead PM, product founder, or equivalent senior individual contributor responsible for a business-critical platform.
- Evidence of defining strategy across multiple teams and delivering sustained customer and commercial outcomes — not merely shipping individual AI features.
- Experience building production AI products that combine answers, context, tools, workflows, and actions.
- Strong understanding of the distinction between:
- Language generation and grounded answers
- Retrieval quality and final-answer quality
- A chat interface and an agentic operating system
- A successful demonstration and a dependable production product
- Model intelligence and complete system reliability
- Technical fluency across several of the following:
- Retrieval-augmented generation
- Document ingestion and parsing
- Chunking strategies
- Embeddings and vector search
- Keyword and hybrid retrieval
- Metadata filtering and query rewriting
- Re-ranking and source attribution
- Structured-data retrieval
- Knowledge freshness and lifecycle management
- LLM orchestration, tool and function calling, agent routing
- Context management and memory systems
- Evaluation and observability
- APIs and webhooks
- Strong judgment around AI evaluation — able to define how retrieval, answers, plans, tools, actions, artefacts, and memory should be tested and monitored.
- Strong systems thinking — able to connect identity, permissions, agency and location context, CRM data, Knowledge Bases, memory, tools, workflows, billing, and reporting into a coherent platform.
- Experience designing products where AI can change business data or execute consequential actions.
- Strong instincts around permissions, approvals, auditability, reversibility, sensitive information, and customer trust.
- Experience simplifying technically sophisticated systems for non-technical users.
- Experience with multi-tenant SaaS and the operational needs of agencies, resellers, franchises, enterprises, or multi-location businesses.
- Strong customer instincts and willingness to personally inspect retrieval outputs, AI conversations, tool traces, failures, support issues, and user configurations.
- Strong analytical capability — able to separate model failures, retrieval failures, tool failures, UX problems, and expectation mismatches.
- Commercial judgment across activation, retention, packaging, usage-based pricing, infrastructure cost, AI cost, gross margin, and ecosystem economics.
- Ability to influence multiple senior teams without relying on direct reporting authority.
- Excellent written communication — able to produce clear strategy documents, platform contracts, architectural decisions, evaluation plans, product requirements, executive updates, and launch narratives.
- High ownership, intellectual honesty, and execution velocity in a remote-first, high-context environment.
- Comfortable making difficult prioritisation decisions across answer quality, action coverage, customer experience, platform extensibility, privacy, reliability, technical debt, revenue, and cost.
Bonus Points For
- Experience building an AI copilot, enterprise assistant, workplace assistant, CRM assistant, browser agent, or natural-language interface for a complex software platform.
- Experience building enterprise-search or knowledge-management products.
- Experience with retrieval architectures involving dense and sparse search, embeddings, hybrid retrieval, re-ranking, query decomposition, metadata filtering, citations, and evaluation.
- Experience building ingestion pipelines for websites, JavaScript applications, PDFs, Word documents, cloud drives, tables, databases, and APIs.
- Experience with structured-data question answering, semantic table search, text-to-SQL, analytics copilots, or business-intelligence assistants.
- Experience designing memory or personalisation systems with user controls, scope, lifecycle, and privacy protections.
- Experience building agent-tool ecosystems using function calling, MCP, plugins, APIs, workflow nodes, or capability registries.
- Experience building agent evaluation systems using test datasets, simulations, production telemetry, human review, user feedback, and business-outcome measurement.
- Experience with permission-aware AI systems, approval workflows, audit logs, bulk actions, undo, or regulated enterprise workflows.
- Experience with scheduled agents, proactive assistants, recurring reports, or event-driven AI work.
- Experience with rich AI outputs such as charts, tables, diagrams, forms, approval cards, editable artefacts, images, or generated media.
- Experience building browser automation, robotic process automation, desktop assistants, or cross-application agents.
- Experience building products similar to Glean, Microsoft Copilot, Salesforce Einstein, HubSpot Breeze, Notion AI, Intercom Fin, Zendesk AI, ServiceNow Now Assist, Google Vertex AI Search, Amazon Bedrock Knowledge Bases, or comparable platforms.
- Experience building APIs, developer tools, marketplaces, reusable templates, or partner ecosystems.
- Experience building for agencies, SMBs, franchises, local businesses, or multi-location operators.
- Experience with AI usage-based pricing, inference cost management, vector-storage economics, model routing, gross-margin optimisation, rebilling, or fair-use controls.
- Familiarity with metrics such as Knowledge Base creation completion, ingestion success rate, retrieval recall, top-k precision, grounded-answer rate, tool-selection accuracy, action success rate, task completion rate, repeat usage, P95/P99 latency, cost per successful outcome, and support-ticket rate.
Why Join HighLevel?
At HighLevel, we foster an exciting and dynamic work environment driven by a passionate team. We believe in a collective responsibility where no task is considered someone else's job. Our unwavering focus is on providing value to our users, and we achieve this by delivering solutions swiftly through lean principles, allowing us to bring products to market in weeks rather than quarters.
Every good idea is put to the test, ensuring that we maintain a high standard of innovation. We prioritise the well-being of our team, recognising that by taking care of them, they can better serve our users. We embrace the concept of continuous and iterative improvement, understanding that progress is an ongoing journey. We are also a well-funded and profitable company.
Join us at HighLevel, and you will have the opportunity to learn the intricacies of scaling a B2B SaaS startup and develop impactful products that cater to the needs of our customers.
EEO Statement
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