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The Role

As an AI Field Engineer for Microsoft Foundry, you will be one of the technical owners of Fireworks' most strategic partnership. You'll work closely with Microsoft's field teams, Azure-aligned ISVs, and the SIs that run enterprise AI transformation programs to make Fireworks the default inference and fine-tuning layer in every Azure AI architecture your partners touch. The role sits at the intersection of engineering, partner development, and customer delivery. You build reference architectures, run benchmarks, debug production integrations, and co-develop POCs - all while holding your own in executive-level conversations about strategy, roadmap, and business outcomes.

You spend most of your time building and enabling. You ship code, run joint POCs with Microsoft field teams, and architect deployments that span Azure Foundry and Fireworks. But you also lead discovery conversations, align partner stakeholders, and translate field signals into product improvements that compress the feedback loop from partner to roadmap. 

The Segment

As a Field Engineer aligned with our Partnerships team you own the technical relationship between Fireworks and the Microsoft ecosystem, Azure field teams, ISVs building on Azure Foundry, and the SIs that deliver AI transformation programs on Azure. The Microsoft partnership is a core go-to-market bet: clients like UIPath, Stack Blitz, Motif run via Fireworks on Foundry.. Your job is to scale that pattern across the partner ecosystem. These engagements involve large, multi-stakeholder organizations, so you will need to navigate both the enterprise buyer (IT, security, compliance) and the builder (ML engineers, platform teams, app developers), while building the trusted-advisor relationships inside Microsoft's field that multiply your reach.

What You'll Work On

Technical Delivery and Deployment

• Be the technical lead on co-sell motions with Microsoft - joint reference architectures, Azure Foundry 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 Microsoft's 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.
• 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.

On-Target Expectations (Plus Equity)

$200,000 - $260,000 USD

Total compensation also includes meaningful equity in a fast-growing startup, along with a competitive salary and comprehensive benefits package. Base salary is determined by a range of factors including individual qualifications, experience, skills, interview performance, market data, and work location.

Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.