Field Execution Partner, AI Field Engineering

The Role

We're seeking a technical, field-tested operator to serve as the dedicated execution and enablement partner for our AI Field Engineering (AFE) team. As part of Revenue Operations, you'll design the programs and lead the change that keep this team sharp, consistent, and ahead of the curve as Fireworks scales.

This role requires deep domain experience as a former AFE, Solutions Architect, or Sales Engineer. You'll pair technical credibility with business-operations rigor to bring a clear point of view on what great looks like and the program discipline to make changes stick globally. You'll also use your own AI fluency to bring agentic workflows into how AFEs work day to day. Your success will be measured in AFE outcomes: time to first technical win and ramp compliance for new hires, and productivity across the ramped team.

Please Note: This is an individual contributor role reporting to the Head of GTM Enablement. Expect ~15–20% travel for team events and trainings.

What You'll Own

1. Embed in AFE leadership and drive critical change management initiatives:
   Take a standing seat in AFE leadership forums, where you'll help shape the segment's go-to-market strategy alongside your RevOps counterpart. As the operating model evolves, you'll drive the execution of the team's most critical initiatives, whether that's a multi-quarter transformation or a fix shipped in days.

2. Design and deliver ongoing enablement programs for tenured AFEs:
   Keep experienced AFEs ahead of a fast-moving product with a continuous cadence of AFE-specific enablement, spanning technical trainings, process execution, skills workshops, and manager-led certifications.

3. Optimize the AFE ramp program and own the outcomes for new AFEs:
   Audit and continuously improve the AFE onboarding experience, from our Firecademy onboarding program through full ramp, and make sure every new AFE ramps consistently, wherever they sit. You'll be measured on time to first technical win, ramp compliance, and ramp productivity.

4. Surface and scale AFE best practices:
   Pinpoint what top AFEs do differently through call reviews, field conversations, and performance data. Then scale those patterns across the team with programs like peer showcases, POC reviews, and a curated call library, and feed what you learn back into the broader go-to-market team.

5. Build the AI tools AFEs use on live deals:
   Design and ship AI agents that work inside the AFE workflow, such as POC preparation, eval design, and technical objection handling, so AFEs move faster with customers.

Minimum Qualifications

• 8+ years of experience, including significant time as an AFE, Solutions Architect, Sales Engineer, or in a similar technical pre-sales role

• Technical depth in AI/ML or developer infrastructure, and the credibility to coach technical sellers who work with technical buyers

• A track record of driving change across a team, with the rigor to define, track, and act on the metrics that prove it worked

• Strong at both content development and live facilitation for technical audiences, from new-hire cohorts to experienced practitioners

• Has personally built AI agents or apps, through prompting, no-code, low-code, or code, to drive seller or operational productivity (engineering degree not required)

• Thrives in ambiguity, high ownership, and the pace of a high-growth startup

• Based in AMER and willing to travel ~15–20%

Preferred Qualifications

• Experience as a first- or second-line leader of a technical pre-sales team, or as Chief of Staff to a GTM leader

• AFE, SA, or SE experience at an AI/ML infrastructure, developer tools, or API-first and consumption-based business, with literacy in usage-based pricing models

• Prior ownership of onboarding, ramp, or enablement programs for technical sellers

• Fluency in enterprise sales methodologies (e.g., Command of the Message, MEDDICC) applied to technical pre-sales

• Familiarity with modern enablement tooling and systems (LMS, CMS, CRM, conversation intelligence)

Why Fireworks?

• Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.