AI Field Engineer (Enterprise)

TL;DR

AI Field Engineer (Enterprise): Building and deploying production-grade generative AI systems for enterprise customers with an accent on inference optimization, fine-tuning pipelines, and scalable architecture. Focus on bridging the gap between complex AI research and real-world business outcomes through hands-on coding, stakeholder management, and technical discovery.

Location: Must be based in the USA. This role involves both remote work and on-site customer engagements.

Company

Fireworks AI is a high-growth, Series C startup building the industry's fastest and most scalable generative AI inference infrastructure, founded by veterans from Meta PyTorch and Google Vertex AI.

What you will do

• Build end-to-end POCs and MVPs directly within customer codebases and infrastructure.
• Architect inference foundations and tune deployments to meet latency, throughput, and cost targets.
• Guide customers through model selection, fine-tuning strategies (SFT, DPO, RFT), and evaluation methodologies.
• Lead technical discovery and maintain relationships with both ML engineers and executive stakeholders.
• Translate customer pain points and deployment patterns into actionable product roadmap improvements.
• Spend time on-site with customers to build trust and accelerate production deployments.

Requirements

• 5+ years in a customer-facing technical role such as Field Engineer, Solutions Architect, or ML Engineer.
• Strong Python proficiency with experience shipping production-grade code.
• Hands-on experience with the LLM stack, including inference trade-offs and fine-tuning workflows.
• Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure.
• Familiarity with Kubernetes and infrastructure engineering.
• Exceptional communication skills with the ability to present to VPs and debug technical issues with engineers.

Nice to have

• 10+ years of experience in technical field or engineering roles.
• Experience with inference serving frameworks like vLLM, SGLang, or TensorRT-LLM.
• Track record of taking GenAI POCs to production-scale deployments.
• Experience with hyperscaler AI platforms like Azure AI Foundry, AWS Bedrock, or GCP Vertex.
• Experience building agentic systems or AI-native developer toolchains.

Culture & Benefits

• Work at the forefront of AI infrastructure and low-latency inference.
• Collaborate with world-class engineers and researchers from top AI labs.
• High-impact environment with minimal bureaucracy and a focus on shipping results.
• Opportunity to shape the future of how businesses harness generative AI globally.