Lead Forward Deployed Engineer, AI Enablement

Reddit

Confirmed live today High trust
Remote

Quick summary

Work type
Remote
Location
Remote
Salary
$194,600–$272,400 / yr
Posted
8 days ago
Freshness
Confirmed live today

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $196k
This role $234k
$129k $288k
below market most similar roles pay here above market

This role pays more than 78% of similar roles. Most pay $162,000–$230,400 — the blue band above. At the midpoint, this role pays about $234k versus about $196k for comparable roles.

Based on 240 similar postings.

Employer

About Reddit

Reddit is a social news aggregation and discussion platform where users share content, vote on posts, and engage in community conversations across thousands of interest-based forums called subreddits.

Reddit currently has 89 open roles on FindRole.

Listed pay typically runs $216,700–$303,000 across 89 roles with salary data.

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At a glance

TL;DR · Lead Forward Deployed Engineer, AI Enablement

JOB TITLE: Lead Forward Deployed Engineer (AI Enablement) As a Lead Forward Deployed Engineer, you will join the AI Enablement team to own the vision for how applied AI solves business problems across functions like Sales, Marketing, Business Development, and Finance. You will identify high-value opportunities, set technical direction, and build production capabilities including AI applications, agents, skills, connectors, and workflow automations. Your day-to-day involves translating ambiguous business problems into AI-native workflows, prototyping solutions, and integrating APIs, data systems, and enterprise tools. You must possess 8+ years of software engineering experience, specifically with production LLMs, retrieval, structured outputs, evals, and MCP. You will lead engineers through design reviews, mentor technical leads, and establish architecture patterns to improve quality, speed, capacity, and cost while ensuring governance and observability for all deployed workflows.

What you'll do

  • Build AI applications, agents, skills, connectors, and workflow automations to solve business problems.
  • Translate ambiguous business problems into AI-native workflows and technical delivery plans.
  • Prototype solutions quickly and productionize priority workflows within weeks.
  • Integrate approved data, tools, APIs, and actions into safe end-to-end workflows.
  • Define evals, review gates, human-in-the-loop controls, and observability for AI systems.
  • Organize and sequence engineers' work while clarifying ownership, dependencies, and milestones.
  • Mentor engineers and technical leads through design and code reviews to strengthen production judgment.
  • Turn local wins into reusable agents, templates, connectors, and platform patterns.

What we're looking for

  • 8+ years of experience building software, including automations, workflows, AI applications, agents, or internal tools.
  • Strong software engineering fundamentals and production judgment to navigate ambiguous business and technical discovery.
  • Experience with MCP, reusable tool integrations, and enterprise identity and access patterns.
  • Experience integrating APIs, data systems, enterprise tools, and workflow actions.
  • Hands-on experience building and deploying AI agents with tool use, orchestration, state, and failure handling.
  • Production LLM experience with retrieval, structured outputs, evals, monitoring, and controls.
  • Demonstrated technical leadership through architecture decisions, reviews, mentoring, and cross-team influence.
  • Experience creating a component, engineering practice, or architectural pattern adopted beyond your own project.
  • Experience in applied AI, forward-deployed engineering, internal platforms, or enterprise workflow modernization (preferred).
  • Experience improving engineering practices across a team or multiple teams (preferred).
  • Experience delivering business-critical workflows with auditability, sensitive data, or consequential automated actions (preferred).
  • Experience measuring workflow gains in quality, speed, capacity, or cost (preferred).
  • Experience driving AI adoption through user feedback, onboarding, and training (preferred).

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