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Buzz Solutions builds visual AI for the power grid. The PowerAI platform analyzes utility inspection imagery (drone, helicopter, fixed camera) in under a second—replacing manual review that takes 1–2 minutes. The company works across transmission, distribution, substations, and solar, helping major North American utilities detect failing components before outages and compress inspection cycles from years to weeks. Buzz recently closed a $20M Series A.
This is the company's first Forward Deployed Engineer role—a hands-on technical partner embedded with customers and the go-to-market team, bridging Sales, Product, and Engineering. You are not a program or product manager; you write the integration, join customer calls, and own the technical execution end-to-end.
Before the deal closes, you scope implementations: join discovery calls with utility engineering and operations teams, size what deployment actually requires, run pilots on customer imagery and assets, and clear technical objections (architecture, security, data handling) that stall regulated utility sales cycles.
After the deal, you deploy: stand up PowerAI in customer environments, build integrations into their existing systems, and use AI workflows to deliver in weeks what traditionally took months.
Approximately 80% of your time is customer-facing (pre-sales scoping and deployment). The remaining 20% is internal: building reusable deployment tooling and AI agents, and translating field patterns into product and engineering improvements.
You report directly to the CTO and work in partnership with Sales, Product, Solutions, and core Engineering. As the first FDE, you'll help define how the function operates and lay the foundation for a broader Forward Deployed Engineering team as the company scales.
Key responsibilities:
- Ride alongside Sales as the technical partner on discovery calls; understand customer inspection workflows and data reality, and translate them into what a Buzz deployment looks like.
- Scope and size implementations before signature: integration effort, data requirements, timeline, and feasibility.
- Run pilots and proofs of value using customer imagery and assets, not canned demos.
- Clear technical objections: handle architecture questions, security review, and data-handling scrutiny from regulated utility buyers.
- Deploy PowerAI in customer environments: build integrations, configure workflows, and support go-live.
- Translate field patterns into reusable deployment capabilities and product feedback.
- Build internal tooling and AI agents to accelerate future deployments.
Requirements:
- 5+ years of software engineering experience, with a track record of shipping production systems.
- Proficiency in Python and at least one other systems language (Go, Rust, C++).
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Strong communication skills; ability to translate between technical and non-technical stakeholders.
- Comfort with ambiguity and ownership of end-to-end problems in a small, fast-moving team.
- Experience in utilities, energy, or another regulated industry with long enterprise sales cycles (preferred).
- Familiarity with computer vision, geospatial data, or large-scale imagery pipelines (preferred).
- Experience as the first or earliest technical hire in forward deployed engineering, customer engineering, or implementation—building a function rather than joining one (preferred).
- Background in RevOps or internal tooling that earned real, sustained adoption (preferred).
