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Fireworks is a Series D AI platform company (valued at $17.5B) that enables enterprises to build, train, and serve specialized AI models tailored to their data and workflows. Backed by AMD, NVIDIA, Sequoia, Benchmark, and other top-tier investors, Fireworks powers production AI across text, image, embedding, audio, and multimodal workloads with hundreds of state-of-the-art open models.
As an AI Field Engineer for Strategic Partnerships, you will be a technical owner of Fireworks' most strategic partnerships (e.g., Microsoft Azure Foundry). You sit at the intersection of engineering, partner development, and customer delivery—building reference architectures, running benchmarks, debugging production integrations, and co-developing POCs while engaging in executive-level conversations about strategy and business outcomes.
You will spend most of your time building and enabling: shipping code, running joint POCs with partner field teams, and architecting deployments that span strategic partners and Fireworks. You also lead discovery conversations, align partner stakeholders, and translate field signals into product improvements that compress the feedback loop from partner to roadmap.
Your segment covers the technical relationship between Fireworks and partner ecosystems, partner field teams, ISVs building on strategic platforms, and systems integrators delivering AI transformation programs. You will navigate both enterprise buyers (IT, security, compliance) and builders (ML engineers, platform teams, app developers) while building trusted-advisor relationships inside partner organizations.
Key responsibilities include: leading co-sell motions with strategic partners through joint reference architectures and shared POCs; building end-to-end POCs and MVPs alongside partner engineering teams; running load tests and establishing latency, throughput, and cost baselines; deploying and validating new model families on inference frameworks (vLLM, SGLang); guiding customers on model selection and fine-tuning strategy (SFT, DPO, RFT); designing evaluation frameworks for production-quality metrics; owning the feedback loop to surface partner-driven product gaps; shipping external technical content (reference architectures, integration guides, benchmarks); and tracking pipeline health.
Minimum qualifications: 3+ years in pre-sales, partner engineering, forward-deployed, or technical consulting roles; demonstrated ability to build production software with customers and ship code running in production environments; strong Python skills with comfort reading, writing, and debugging production code; familiarity with Kubernetes and infrastructure engineering; hands-on fluency with LLM inference (latency/throughput tradeoffs, batching, quantization, structured outputs).
