AI Field Engineer (AI Natives)

Join Fireworks as an AI Field Engineer, where you'll work closely with ambitious customers and technology partners to turn complex AI problems into production systems. You'll be hands-on in building POCs, MVPs, and production integrations, while also engaging in executive-level conversations about architecture, strategy, and business outcomes. This role requires strong technical skills, exceptional communication abilities, and a passion for AI and engineering.

• Concevoir et mettre en œuvre des preuves de concept (POC) et des produits minimum viables (MVP) en collaboration avec les équipes d'ingénierie des clients.
• Diriger des conversations de découverte structurées pour comprendre les points de douleur des clients et proposer des solutions techniques adaptées.
• Établir et maintenir des relations techniques solides avec les clients, en agissant en tant que pair avec leurs équipes d'ingénierie.

• Exceptional communication: able to run a sharp discovery call, present to a VP, and debug a latency issue with an ML engineer in the same afternoon
• Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure
• Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains
• Demonstrated ability to build production software with customers, not just advise on it. You have shipped code running in someone else's production environment
• Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering
• Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus)
• 5+ years in a hands-on, customer-facing technical role: Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder
• 10+ years in technical field or engineering roles
• Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads
• Prior experience at a company with a forward-deployed or embedded engineering model (Palantir, Scale AI, Anthropic, OpenAI, BCG X, McKinsey Quantum Black, AI Native startups with FDE motions)
• Prior experience as a technical founder or early engineer at an AI-native company is a strong signal
• Track record taking GenAI POCs from prototype to production-scale deployments
• Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex)