AI Field Engineer - AI Natives

Fireworks AI is building the future of generative AI infrastructure, delivering high-quality models for scalable inference. The AI Field Engineer will work closely with customers to develop production systems, engaging in both technical delivery and strategic conversations.

Responsibilities:

• Build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints
• For customers whose core product is built on GenAI, architect the inference foundations that capability depends on, and size deployments so they can scale in their market without infrastructure becoming the bottleneck
• 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
• 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
• Help them bake frontier model capabilities into their core offering and turn that into a durable competitive edge
• Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria before proposing solutions
• Own the technical relationship from first engagement through production deployment. Embed with their engineering team as a peer, your credibility comes from what you build alongside them
• Spend time on-site with customers. Build trust and momentum in person, embedding with their teams where the work happens
• Identify recurring customer pain points and translate them into concrete product proposals, working directly with engineering and product to ship fixes and features
• Codify repeatable deployment patterns and contribute them back to internal tooling, documentation, and the platform itself
• Feed customer signals (deployment patterns, failure modes, feature gaps) back into the product roadmap with specificity and urgency