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Manager, Field Engineering

Job in San Mateo, San Mateo County, California, 94409, USA
Listing for: Fireworks
Full Time position
Listed on 2026-10-05
Job specializations:
  • Software Development
    Software Project Mgr/ Lead, AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 180000 - 240000 USD Yearly USD 180000.00 240000.00 YEAR
Job Description & How to Apply Below
About Us:

Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads.

Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that’s building the future of enterprise AI.

We’re hiring a Manager, Field Engineering to lead a 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 a distributed team of hands-on engineers while staying deeply technical yourself, leading engagements end-to-end at some of the most ambitious AI-native companies and enterprises.

As Manager you will be a critical link between the field and the rest of the company: coaching your field engineers in real time, unblocking complex evaluations, and ensuring every customer engagement meets a high technical bar across latency, throughput, cost, security, and scalability. You’ll work closely with regional sales leadership to scale the engagement model, codify the playbook, and build the team.

This is a hands-on leadership role. Your credibility with field engineers comes from having built production AI systems with customers, not just having managed people who did. You will still get in the weeds when the stakes are highest with shipping POCs, performance optimizations, co—lead training engagements with our research team and debugging alongside your team.

What You’ll Do
  • Lead, coach, and grow a distributed team of Field Engineers — hiring, onboarding, developing, and holding a high bar for technical excellence and customer outcomes
  • Own your team’s engagement portfolio: allocate the right engineers to the right pursuits and ensure consistent execution across discovery, demos, POCs, and production integrations
  • Lead complex evaluations end-to-end (discovery → architecture → POC → production plan), personally stepping in on the highest-stakes or most technically challenging deals
  • Coach AEs and Field Engineers in real time to improve deal quality and close outcomes — reviewing architectures, sitting in on calls, and running sharp post-mortems on wins and losses
  • Build and refine the Field Engineering playbook: discovery frameworks, POC templates, reference architectures, and reusable field artifacts
  • Serve as the voice of your team and customers internally — systematizing field insights, influencing the product/engineering roadmap, and translating recurring pain points into concrete platform improvements
  • Partner with revenue leadership on pipeline health, forecast calls, and territory planning for your region
  • Stay hands-on when it matters: build/ship alongside your engineers (POCs/MVPs, load testing, eval + fine-tuning pipelines, model-serving choices across vLLM/SGLang/TensorRT-LLM) and ensure strong post-sales handoffs for onboarding and adoption
  • Track and improve your team’s operating metrics — win rates and velocity, POC cycle time, utilization, and customer adoption outcomes
You May Be a Fit If
  • 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 a strong technical foundation and fluency in the LLM stack
    : inference trade-offs, model serving, fine-tuning workflows (SFT; DPO/RFT a plus),…
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