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Beacon Software

Forward Deployed Engineer

San Francisco, CAfulltimemidAdded 1 month ago

About this role

Beacon Software seeks Forward Deployed Engineers to embed with portfolio companies and build agentic software systems that solve high-leverage operational problems. You'll identify key workflows where AI can drive business impact, ship working systems, and translate learnings into reusable playbooks across the portfolio.

What you'll do

  • Embed with portfolio companies to understand their operating model and identify 2-3 high-leverage workflows for agentic automation
  • Build end-to-end agentic software solutions including integrations, prompts, evaluations, and UI surfaces
  • Monitor deployed systems post-launch, tune prompts, and adjust autonomy levels based on performance and trust
  • Codify successful patterns into reusable playbooks for other companies in the same vertical
  • Serve as technical relationship owner with portfolio company leadership, communicating value and managing expectations
  • Work across backend, frontend, and data layers using existing Beacon platforms and extending them as needed

What they're looking for

  • Full-stack software engineering (TypeScript, Python, React, SQL)
  • LLM-native development and prompt engineering
  • Agentic workflow design and evaluation frameworks
  • Data integration and API development
  • Business acumen and P&L literacy
  • Customer discovery and relationship building
  • Autonomous decision-making and ownership
  • Technical communication and documentation
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Beacon Software

Beacon Software builds agentic software systems and internal platforms that improve operations across portfolio companies, focusing on AI-driven automation and data integrations. The company is hiring Forward Deployed Engineers to embed with portfolio companies and ship production systems, as well as Software Engineering Interns to contribute to their internal operational platform.

View all jobs at Beacon Software

Likely interview questions

  • Tell us about a time you shipped an LLM-native feature or system in production. Walk us through the eval strategy you used to keep it from hallucinating or going off the rails.
  • Describe a situation where you embedded deeply with a customer or stakeholder to understand their real problem, then discovered it was different from what they initially told you. How did you handle it?
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