AI Field Engineer

What You'll Work On

Technical Delivery and Deployment

• Be the technical lead on co-sell motions with Strategic Partners — joint reference architectures, partner integration patterns, and shared POCs for strategic accounts.
• Build end-to-end POCs and MVPs alongside partner engineering teams, working inside their codebases, infrastructure, and constraints.
• Run load tests and establish latency, throughput, and cost baselines against realistic customer traffic profiles, and tune deployments to hit those targets.
• Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns across workloads.

Model Strategy and Fine-Tuning

• Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology.
• Build and run fine-tuning pipelines directly with customers, navigating trade-offs between model families, compute cost, and quality targets.
• Design and implement evaluation frameworks that measure production-quality metrics, not just benchmark scores.

Product Feedback and Platform Improvement

• Own the feedback loop — surface partner-driven product gaps to Fireworks engineering, and translate the roadmap back into partner messaging.
• Ship external technical content: reference architectures, integration guides, and benchmark posts that make it easy for partners to win deals with us.
• Track pipeline health; flag risks and opportunities to Field leadership weekly.

What We're Looking For

Minimum Qualifications

• 3+ years in a pre-sales, partner engineering, forward-deployed, or technical consulting role.
• 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.
• Hands-on fluency with LLM inference: latency/throughput tradeoffs, batching strategies, quantization, structured outputs, function calling. You can explain why 50ms p99 matters to an enterprise CTO.
• Real experience with fine-tuning — LoRA at minimum, RFT a strong plus. You understand when SFT is enough and when it isn't.
• Deep familiarity with the Azure AI stack: Azure Foundry, Azure OpenAI Service, Azure ML, AKS, Entra/RBAC for AI workloads. You know where Fireworks fits and where it doesn't.
• 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.

Preferred Qualifications

• 5+ years in technical field or engineering roles where you've owned a technical relationship with a hyperscaler or major SI, not just supported one.
• Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads.
• Prior role at a hyperscaler, AI-native cloud, or inference provider.
• Deep familiarity with other strategic partner stacks: Platforms for AI workloads, network, and identity integration patterns. You know where Fireworks fits and where it doesn't.
• Experience with agentic frameworks (LangChain, LlamaIndex, or custom tool-use pipelines) — you understand how inference latency and reliability shapes agent behavior at scale.
• Background in model evaluation — you understand why benchmark gaming is rampant and what rigorous evals actually look like.
• You've written a technical blog post or reference architecture that people actually read.
• Track record taking GenAI POCs from prototype to production-scale deployments.