Manager of Field Engineering

Join Fireworks as a Manager of Field Engineering, where you'll lead a team of Field Engineers in driving technical evaluations and production adoption of our inference and fine-tuning platform. This hands-on leadership role requires a strong technical background, experience in managing field engineering teams, and a passion for AI and enterprise software.

• Lead and manage a distributed team of Field Engineers, driving technical evaluations and production adoption of Fireworks' inference and fine-tuning platform.
• Oversee the end-to-end engagement process for complex evaluations, ensuring high technical standards across latency, throughput, cost, security, and scalability.
• Collaborate closely with regional sales leadership to scale the engagement model, codify the playbook, and build the team, while maintaining a hands-on approach to technical challenges.

• Willingness to travel (up to ~30%) for customer engagements and team onsites
• You have demonstrated ability to build production software with customers, not just advise on it — you have shipped code running in someone else's production environment and can still do it when a deal demands it
• You have a builder mindset: you thrive in fast-moving, startup environments and enjoy creating structure where little exists
• You have 8+ years of overall experience, including 2+ years managing Field Engineering, Solutions Engineering, Forward Deployed Engineering, or Pre-Sales teams, with hands-on experience in enterprise software or AI infrastructure
• You have proven your ability to partner effectively with Sales while maintaining technical integrity and customer trust in high-stakes deal environments
• You have strong communication skills — able to run a sharp discovery call, present to a VP or CTO, and debug a latency issue with an ML engineer in the same afternoon
• You have a track record of developing engineers — coaching, giving direct feedback, and growing individual contributors into senior and lead roles
• You have a strong technical foundation and fluency in the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT; DPO/RFT a plus), and deploying models on GPU infrastructure across AWS, Azure, and GCP