AI Field Engineer

Responsibilities

• Build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases and infrastructure
• Architect inference foundations and size deployments to ensure scalability
• Run load tests and tune deployments for latency, throughput, and cost targets
• Deploy and validate new model families on inference frameworks like vLLM and SGLang
• Guide customers on model selection, fine-tuning strategies (SFT, DPO, RFT), and evaluation methodologies
• Lead technical discovery conversations and own the technical relationship from engagement to production
• Translate customer pain points into concrete product proposals for the engineering and product teams

Requirements

• 5+ years in a hands-on, customer-facing technical role (e.g., Forward Deployed Engineer, Solutions Architect, or ML Engineer)
• Proven ability to ship production software within customer environments
• Strong Python skills and familiarity with Kubernetes and infrastructure engineering
• Working knowledge of the LLM stack, including inference trade-offs and fine-tuning workflows
• Experience with cloud infrastructure (AWS, Azure, GCP) and GPU deployment
• Experience building or integrating agentic systems or AI-native toolchains

Preferred Qualifications

• 10+ years in technical field or engineering roles
• Experience with inference serving frameworks such as vLLM, SGLang, or TensorRT-LLM
• Prior experience at companies with embedded engineering models (e.g., Palantir, Scale AI, Anthropic)
• Experience with hyperscaler AI platforms like Azure AI Foundry, AWS Bedrock, or GCP Vertex

About the Company

Fireworks is building the future of generative AI infrastructure, delivering high-quality models with industry-leading inference speed. A Series C company valued at $4 billion, Fireworks is backed by top investors including Benchmark and Sequoia.