Director, GTM AI Engineering

About the role:

Samsara’s Machine Learning, AI, Analytics & Data (MAAD) team builds the intelligence and technology that helps our go-to-market teams work faster, make better decisions, and spend more time with customers.

As the Director, GTM AI Engineering you’ll run our AI Engineering team for GTM. You’ll work closely with Data Science, Analytics, and Data Engineering to serve Sales, Customer Success, Support, Enablement, and GTM leadership. This is a hands-on Director role. Expect to spend about 70% of your time building (prototyping agents, writing code, designing evals) and about 30% leading the team, setting strategy, and influencing stakeholders.

You’ll build intelligent workflow experiences. That ranges from AI agents and copilots that take manual work off sellers’ plates, to signals for propensity and prioritization, to the data, evaluation, and measurement foundations that make these products reliable and trusted.

The ideal candidate is AI-native. You already run your own work through agents every day, you’ve shipped AI products to production, and you get as excited about a seller’s first “wow, this saved me an hour” moment as you do about a clean architecture.

This is a remote position open to candidates residing in the US.

In this role, you will:

• Build hands-on. Prototype, ship, and iterate on AI agents, copilots, and workflow automations for GTM teams, from the first demo to a production system people rely on.
• Build the platform, not just the apps. Own the architecture of the shared GTM AI platform: orchestration, LLM and tool gateways, identity and access, and the MCP and SDK contracts that let the GTM web app, Salesforce, Slack, desktop, and Claude plugins share one set of governed capabilities.
• Make evals a first-class discipline. Design evaluation frameworks for agent and LLM behavior, including golden datasets, LLM-as-judge, human review loops, and regression suites, and run them in development and production. Use transcript analysis and eval results to find failures, guide what to fix first, and show quality improving over time.
• Lead with AI day to day. Use agents, coding assistants, and agent harnesses in your own daily work, and build the shared tooling, patterns, and harnesses that make the whole team faster.
• Stay ahead of the curve. Track new models, agent frameworks, MCP and tool ecosystems, and context engineering, and turn them into practical recommendations for Samsara's GTM use cases: what to adopt, what to test, and what to ignore. Make clear build-versus-buy calls on vendor tools, not just models.
• Drive adoption with the field. Be the go-to advocate for AI across Sales, CS, and Support. Run demos, earn credibility with reps and frontline managers, collect real feedback, and turn skeptics into power users.
• Set the strategy. Own the technical and product strategy for GTM AI, including the data foundations, instrumentation, governance, and observability these products depend on. Decide which prototypes move onto the shared platform, which merge, and which retire. Own model routing across vendors and the per-user spend controls that keep AI cost in line with value. Influence senior leaders on AI priorities, investment, and the operating model.
• Ship revenue-critical intelligence. Own the forecasting, propensity, and CRM data-quality work that sales leadership acts on, and keep it calibrated, validated, and clear about its limits. Be comfortable across data and analytics engineering, and act as a business leader when the work calls for it.
• Partner across GTM, Product, and Engineering. Work with Sales, Customer Success, Support, Enablement, Operations, Product, and Engineering leaders to find high-value opportunities, define success metrics, and turn them into durable products.
• Build for trust. Put a pragmatic responsible-AI approach in place: safety, privacy, reliability, and human-in-the-loop workflows with clear approval and escalation paths. Agents that act on CRM and customer data run with per-user, least-privilege permissions, and changes to systems of record get human approval first.
• Automatically included
• All Roles: Champion, role model, and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices
• People Manager Roles: Hire, develop and lead an inclusive, engaged, and high performing AI Engineering team

Minimum requirements for the role:

• 10+ years across software engineering, machine learning, data science, or data platforms, including meaningful leadership experience. You still write code regularly.
• Bachelor’s degree or equivalent relevant work experience.
• Hands-on experience building and shipping LLM applications and agentic workflows to production, including tool use, retrieval, context engineering, and production reliability.
• Production full-stack and cloud engineering experience (for example Python or TypeScript, AWS, and Databricks), including owning systems that other teams depend on.
• Demonstrated experience designing and running evaluation frameworks for LLM or agent systems, and using them to drive quality decisions.
• Daily, fluent use of AI agents and coding assistants in your own work, and a point of view on how they change the way teams build.
• A track record of shipping internal or customer-facing AI/ML products with measurable business impact.
• Experience with predictive modeling, experimentation, analytics, and data engineering foundations.
• Strong product judgment and the ability to turn ambiguous business problems into simple, valuable solutions.
• Excellent communication with both executives and frontline users. You can demo to a sales floor and present to a VP on the same day.
• A people-first leadership style with a track record of hiring, developing, and keeping strong technical talent.

An ideal candidate also has:

• Experience building AI or decisioning products for Sales, Customer Success, Support, Marketing, or other GTM functions, ideally in a fast-growing enterprise SaaS environment.
• Experience with CRM, customer data, support, and knowledge-management systems (such as Salesforce, Gong, Gainsight, Zendesk, and Databricks), and connecting agents to them (for example, through MCP servers or similar integrations).
• Experience with agent SDKs and harnesses (such as Claude Code / Agent SDK, OpenAI Agents SDK, LangGraph, or similar) and LLM observability tooling, eval tooling such as Braintrust or MLflow, and model APIs from Anthropic, OpenAI, and Google.
• Experience operating AI products at scale, including cost management, monitoring, and governance.
• A visible habit of learning in public, such as writing, talks, open-source work, or internal “what’s new in AI” sessions.