AI Field Engineer (AI Natives)

Role Overview

Fireworks AI is hiring an AI Field Engineer (AI Natives) to work directly with AI-native customers. You’ll build POCs, MVPs, and production integrations, and participate in executive-level discovery and architecture conversations—translating customer pain points into deployable AI systems.

Key Missions

• Build POCs, MVPs, and production integrations in collaboration with customers’ engineering teams.
• Lead structured discovery conversations to identify customer pain points and propose solutions.
• Own the technical relationship end-to-end, from first engagement through deployment in production, working closely with the client’s engineering team.

Responsibilities

• Deliver customer-facing, hands-on engineering work (not only advisory).
• Run sharp discovery calls with stakeholders up to VP level.
• Troubleshoot and debug production issues (including ML/latency problems) in collaboration with customer engineers.
• Support model deployment and serving in real workloads.

Requirements

• Strong Python skills for reading, writing, and debugging production code.
• Experience with LLM stack and model serving, including:
  • Fine-tuning workflows (at least SFT; DPO/RFT is a plus)
  • Inference trade-offs and production serving considerations
• Cloud infrastructure experience and deploying models on GPU infrastructure.
• Familiarity with Kubernetes and infrastructure engineering.
• Demonstrated ability to ship production software in a customer’s environment.
• 5+ years in a hands-on, customer-facing technical role (examples given: Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, technical founder).
• Exceptional communication skills, including running discovery calls, presenting to executives, and debugging technical issues quickly.

Nice-to-haves / Additional Signals

• Experience with agentic systems, tool-use chains, or AI-native developer toolchains.
• 10+ years in technical field or engineering roles.
• Experience with inference serving frameworks: vLLM, SGLang, TensorRT-LLM.
• Prior experience with forward-deployed/embedded engineering models (e.g., Palantir, Scale AI, Anthropic, OpenAI, BCG X, McKinsey Quantum Black, or similar AI-native startups).
• Track record taking GenAI POCs to production-scale deployments.
• Prior technical founder / early engineer experience at an AI-native company.
• Hyperscaler AI platform experience: Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex.