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Fireworks is a Series D AI infrastructure platform enabling companies to build, train, and serve specialized AI models tailored to their data and workflows. The company is backed by AMD, NVIDIA, Sequoia, Benchmark, and other top-tier investors, and is valued at $17.5 billion.
You will lead a distributed team of Field Engineers driving technical evaluations and production adoption of Fireworks' inference and fine-tuning platform. This is a player-coach role: you'll manage and grow your team while staying deeply technical, leading end-to-end engagements with ambitious AI-native companies and enterprises.
Key responsibilities:
- Lead, hire, onboard, and develop a distributed Field Engineering team, maintaining high standards for technical excellence and customer outcomes
- Own your team's engagement portfolio, allocating engineers strategically across discovery, demos, POCs, and production integrations
- Lead complex technical evaluations end-to-end, personally stepping in on the highest-stakes or most technically challenging deals
- Coach Account Executives and Field Engineers in real time to improve deal quality and close outcomes
- Build and refine the Field Engineering playbook: discovery frameworks, POC templates, reference architectures, and reusable artifacts
- Serve as the voice of your team and customers internally, systematizing field insights and influencing the product/engineering roadmap
- Partner with revenue leadership on pipeline health, forecasting, and territory planning
- Stay hands-on: build and ship alongside your engineers (POCs, load testing, eval and fine-tuning pipelines, model-serving choices across vLLM/SGLang/TensorRT-LLM)
- Track and improve team operating metrics: win rates, velocity, POC cycle time, utilization, and customer adoption outcomes
This role demands credibility earned in the codebase and customer infrastructure. You will compress the feedback loop from field to roadmap and treat every customer deployment as an opportunity to improve the platform.
Requirements:
- 8+ years of overall experience, including 2+ years managing Field Engineering, Solutions Engineering, Forward Deployed Engineering, or Pre-Sales teams, with hands-on enterprise software or AI infrastructure experience
- 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
- Demonstrated ability to build production software with customers—you have shipped code running in someone else's production environment and can still do it when a deal demands it
- Track record of developing engineers: coaching, giving direct feedback, and growing individual contributors into senior and lead roles
- Proven ability to partner effectively with Sales while maintaining technical integrity and customer trust in high-stakes deal environments
- Strong communication skills: able to run sharp discovery calls, present to VPs/CTOs, and debug latency issues with ML engineers
- Builder mindset: thrive in fast-moving startup environments and enjoy creating structure where little exists
- Willingness to travel up to ~30% for customer engagements and team onsites
