Senior Forward Deployed Engineer

About the Role

This is our first Forward Deployed Engineer - a hands-on technical partner embedded with customers and our go-to-market team, and a bridge between Sales, Product, and Engineering.

Before the deal, you're scoping: on discovery calls with utility engineering and operations teams, sizing what an implementation actually takes, running pilots, and clearing the technical objections that stall a sales cycle. After the deal, you're deploying: standing up PowerAI in customer environments, building the integrations into the systems they already use, and leaning on AI workflows to deliver in weeks what traditionally took months.

This is a hands-on role, not a program or product management one. You'll be in the systems, writing the integration, and on the call with the customer's engineers. Nobody hands you a spec, and you don't hand one to someone else.

The role exists for two reasons. Our customers need a technical counterpart who can move at their pace and build alongside them. And the patterns you uncover in the field should make Buzz stronger: solve customer-specific problems quickly, then turn recurring needs into reusable deployment capabilities and clear input for the core product.

Roughly 80% of your time is customer-facing, split across pre-sales scoping and deployment. The remaining 20% points inward: building reusable deployment tooling and AI agents and translating field patterns into product and engineering improvements.

You'll report directly to our CTO and work in tight partnership with Sales, Product, Solutions, and core Engineering. As our first FDE, you'll help define how the function operates and lay the foundation for a broader Forward Deployed Engineering team as we scale.

What You'll Do

Pre-sales partnership

• Ride alongside Sales as the technical partner. Join discovery calls, understand the customer's inspection workflow and data reality, and translate it into what a Buzz deployment looks like for them.
• Scope and size implementations before anything is signed - integration effort, data requirements, timeline, and what's genuinely in and out of reach.
• Run pilots and proofs of value that prove the platform against the customer's own imagery and assets, not a canned demo.
• Clear technical objections. Handle architecture questions, security review, and the data-handling scrutiny that comes with regulated utility buyers.

Deployment and Implementation

• Own deployments end to end - configuration, integration, validation, and handoff - for customers with real operational constraints.
• Build the integrations that connect PowerAI to customer systems: asset management, GIS, inspection data pipelines, and reporting tools.
• Use AI workflows to compress delivery. Turn what would be bespoke engineering work into repeatable, agent-assisted implementation, so delivery speed isn't capped by headcount.
• Find and scope expansion. You'll be closer to the customer's operation than anyone else. Spot the next use case, size what it takes, and hand Sales a qualified, technically validated opportunity.
• Bridge field and core engineering. Solve what you can in the field, identify recurring patterns that belong in the product, and bring back clear, reproducible requirements rather than one-off escalations.

Product and Internal Enablement (~20%)

• Build and ship reusable deployment tooling and AI agents across customer delivery and GTM workflows - and drive the adoption that makes them stick.
• Turn field patterns into product leverage. Package recurring customer needs into reusable assets and clear product/engineering input so each deployment gets faster and the platform gets stronger.

Your First 90 Days

• Days 1-30: Learn the platform deeply, shadow active deployments and sales calls, and meet the accounts you'll support.
• Days 31-60: Scope a live opportunity alongside Sales, and run a deployment end to end as the technical lead.
• Days 61-90: Surface and scope an expansion inside an existing account, and ship your first reusable deployment capability or internal AI agent.

What You'll Bring

• 6+ years in solutions engineering, forward deployed engineering, implementation, or professional services at a technical product company - hands on the keyboard, owning customer outcomes.
• Pre-sales experience. You've partnered with account executives through a sales cycle: running technical discovery, scoping implementations before signature, and being the person the customer's engineers trust in the room.
• Strong Python or TypeScript, and genuine fluency integrating third-party systems via APIs, webhooks, and data pipelines.
• Hands-on experience building with LLMs and agentic systems - prompting, tool use, retrieval, orchestration, and evaluation. You've put something agentic in front of real users, not just prototyped.
• Enterprise credibility. You've navigated security review and architecture scrutiny, and can hold your own with skeptical engineers on the other side of the table.
• An eye for the next opportunity. You notice when a customer problem is something we could solve, and you can scope it well enough for Sales to act on.
• Genuine comfort with ambiguity. You can walk into an undefined problem, pick a direction, and ship.
• Excellent communication. You can explain a technical tradeoff to a utility operations director and a customer constraint to an engineer, and be understood by both.
• Willingness to travel roughly 40% of the time for customer sites and team onsites.

Nice to Have

• Experience in utilities, energy, or another regulated industry with long enterprise sales cycles
• Familiarity with computer vision, geospatial data, or large-scale imagery pipelines
• Experience as the first or earliest technical hire in forward deployed engineering, customer engineering, or implementation - building a function rather than joining one
• Background in RevOps or internal tooling that earned real, sustained adoption

Additional Information

• This position does not include sponsorship for United States work authorization.