Head of Forward Deployed Engineering (FDE)

Beacon Software•San Francisco, CA

12d

About The Position

Beacon is an AI transformation company that owns vertical market software businesses and rebuilds how they run and grow on one AI-native operating platform. Forward Deployed Engineers (FDEs) are the crucial link where this platform meets reality. FDEs embed within product teams, own the customer relationship, implement the platform into real company workflows, and relay challenges back to the core platform team. This role is responsible for leading this critical function, including managing the FDE team, developing the operational doctrine, allocating resources across different company pods, and driving the key metric of labor per company, aiming for continuous reduction while accelerating deployment times. Key responsibilities include establishing and enforcing the FDE doctrine, ensuring each engagement is a defined 'wedge' with clear deliverables and contributes back to the platform through primitives, playbooks, or agent workflows. The role also involves managing capacity allocation across approximately 1,000 engineering weeks annually, prioritizing engagements based on predefined gates (business outcome, portco owner, baseline metric, and shipping feasibility). A strong partnership with pod product leaders is essential for managing a shared backlog, balancing opportunity size and go-to-market timing with technical sequencing and platform contribution. Furthermore, the Head of FDE will act as the primary intake for field-derived patterns, advocating for platform enhancements based on observed needs and driving the creation of shared primitives and standardized workflows. The role also entails hiring and developing senior generalist FDEs in Toronto and San Francisco, with a focus on growing others as a key leadership metric. The Head of FDE is expected to be a player-coach, carrying a significant deployment themselves in the first two quarters.

Requirements

Built and led a forward-deployed, solutions, or embedded engineering function at a company with an AI or data platform.
Managed the transition from individual goal setting to a team with a shared backlog and standard, while retaining talent.
Strong generalist engineering skills, comfortable working in existing codebases within a day.
Shipped LLM-native software in production with evaluations, autonomy gating, and outcome tracking.
Ability to review an FDE's 'wedge' and assess its potential for compounding value.
Operator instincts: understanding of P&L, ability to prioritize based on business impact (e.g., churn-save vs. feature launch), and ability to negotiate with EBITDA-driven GMs.
Experience ruthlessly allocating resources and communicating these decisions plainly.
Proven ability to say 'no' to a business regarding scarce engineering resources while maintaining relationships.
Excellent written communication skills and a commitment to transparent, asynchronous work in public channels with documented decisions.
Experience with wedge memos, GM updates, and post-deployment retrospectives as core work products.

Nice To Haves

Palantir-style FDE background.
Private equity or holdco operating exposure.
Experience within vertical SaaS.
Experience migrating a portfolio of legacy stacks onto shared infrastructure/rails.

Responsibilities

Define and enforce the FDE doctrine, ensuring engagements are scoped as wedges with clear deliverables and contribute back to the platform.
Manage the allocation of engineering capacity across company pods, prioritizing engagements based on business outcomes and strategic value.
Foster strong partnerships with pod product leaders to manage shared backlogs and ensure alignment on technical sequencing and platform contributions.
Act as the primary intake for patterns and feedback from the field to drive platform improvements and the development of shared primitives and workflows.
Hire, manage, and develop a team of senior generalist FDEs, focusing on their growth and the upleveling of others.
Serve as a player-coach, carrying a significant deployment in the first two quarters.
Ensure every engagement leaves the portco's own engineers more capable.
Identify and reshape work that is sliding toward one-off code.
Ensure FDE work is visible to the pod that depends on it.
Drive down labor per portco while increasing deployment speed.