AI Field Engineer - Enterprise

Company: Confidential - Late-stage AI inference platform
Location: US-based, remote-friendly (offices in San Mateo, CA and New York, NY); regular travel to enterprise customers
Compensation: $176,000 - $224,000 base (OTE $220,000 - $280,000) + competitive equity
Employment Type: Full-time
Visa Sponsorship: H-1B transfers and TN; O-1 case by case

About the Company

A late-stage generative AI inference and fine-tuning platform that runs production AI workloads for many of the best-known technology companies, founded by veterans of leading AI framework and cloud AI teams.

The Role

Our client is hiring AI Field Engineers (3+ years) to embed with its most ambitious enterprise customers and turn complex generative AI challenges into production systems, fast. You will be the technical tip of the spear, pairing deep hands-on engineering with the executive presence to earn trust across large organizations and carry deals from first discovery call to production deployment.

What You Will Do

• Lead technical discovery, scope POCs, and run load tests and evaluations to choose the right model architecture and deployment configuration.
• Build end-to-end POCs and production integrations hands-on inside customer environments, working within their infrastructure, security and organizational constraints.
• Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT) and evaluation, moving them from open-model exploration to production at scale.
• Manage multi-stakeholder enterprise relationships: find technical champions and align the right people to move deals forward.
• Feed recurring customer pain points and deployment patterns back into the product roadmap.

What You Bring

• 3+ years in client-facing AI/ML roles (forward deployed, solutions architect, applied AI, sales or customer engineering)
• Shipped AI/ML production code inside a customer's environment, not only advisory work
• Deep hands-on LLM inference and fine-tuning: open-model serving frameworks (vLLM, SGLang, TensorRT-LLM) and SFT at minimum
• Owned the pre-sales field cycle end to end: discovery, POC scoping, evals and model selection
• Strong Python plus GPU/cloud infrastructure (AWS, Azure or GCP) and Kubernetes
• Experience at an AI-native or AI-infrastructure startup, or building AI features in enterprise SaaS
• Executive presence with enterprise customers; willing to travel domestically

Nice to Have

• DPO or RFT fine-tuning experience
• Hyperscaler AI experience (Bedrock, SageMaker, Vertex AI, Azure AI Foundry)
• Navigating enterprise organizations end to end

Interview Process

Take-home assignment, recruiter screen (30 min), culture and live coding (1 hour), discovery and hiring manager (45 min), on-site final loop (about 2 hours), executive interview (30 min).

Tech Stack

Python, vLLM, SGLang, TensorRT-LLM, Kubernetes, AWS, Azure, GCP, Bedrock, SageMaker, Vertex AI, LLM fine-tuning (SFT, DPO, RFT), GPU infrastructure