AI Field Engineer (Enterprise)

TL;DR

AI Field Engineer (Enterprise): Building and deploying generative AI infrastructure for enterprise customers with an accent on inference optimization, model fine-tuning, and production-scale integration. Focus on architecting high-performance solutions, managing executive-level technical relationships, and translating complex customer requirements into platform improvements.

Location: Must be based in the USA (New York, NY; San Mateo, CA; or Remote USA).

Compensation: $200,000–$260,000 USD (plus equity).

Company

A Series C startup building high-performance generative AI infrastructure, founded by veterans of 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 scale deployments to handle production traffic.
• Guide customers on model selection, fine-tuning strategies (SFT, DPO, RFT), and evaluation frameworks.
• Lead discovery conversations and manage technical relationships with stakeholders from ML engineers to VPs.
• Identify recurring customer pain points to drive product roadmap and platform improvements.
• Spend time on-site with customers to build trust and accelerate project momentum.

Requirements

• 5+ years of experience in a hands-on, customer-facing technical role (e.g., Field Engineer, Applied AI Engineer, Solutions Architect).
• Strong Python skills with experience building and debugging production-grade software.
• Working knowledge of the LLM stack, including inference trade-offs, model serving, and fine-tuning workflows.
• Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure.
• Exceptional communication skills, capable of presenting to executives and debugging technical issues with engineers.
• Must be authorized to work in the USA and comfortable with travel for on-site customer engagements.

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 from prototype to production-scale.
• Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock, GCP Vertex).

Culture & Benefits

• Meaningful equity in a fast-growing, well-funded startup.
• Opportunity to work on bleeding-edge AI infrastructure and low-latency inference.
• Collaborative environment with world-class researchers and engineers.
• Comprehensive benefits package.
• Direct impact on the future of AI with minimal bureaucracy.