AI Field Engineer (AI)

TL;DR

AI Field Engineer (AI): Building and deploying production-grade generative AI infrastructure for high-velocity customers with an accent on inference optimization, fine-tuning pipelines, and scalable model serving. Focus on architecting end-to-end solutions, debugging production issues, and translating complex customer requirements into platform-level product improvements.

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

Compensation: $200,000–$260,000 USD + Equity.

Company

A Series C AI infrastructure startup valued at $4B, founded by veterans of Meta PyTorch and Google Vertex AI, focused on high-performance LLM inference and training.

What you will do

• Build end-to-end POCs and MVPs directly within customer codebases and infrastructure.
• Architect inference foundations and size deployments to ensure scalability for GenAI-native products.
• Run load tests and tune deployments using frameworks like vLLM and SGLang to meet latency and throughput targets.
• Guide customers through model selection, fine-tuning strategies (SFT, DPO, RFT), and evaluation methodologies.
• Lead discovery conversations and own the technical relationship from initial engagement to production deployment.
• Translate recurring customer pain points into concrete product proposals and platform improvements.

Requirements

• 5+ years in a hands-on, customer-facing technical role such as Forward Deployed Engineer, Applied AI Engineer, or Solutions Architect.
• Strong Python skills with experience reading, writing, and debugging production code.
• 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 VPs and debugging technical issues with ML engineers.
• Must be based in the USA and comfortable with on-site customer engagements.

Nice to have

• 10+ years in technical field or engineering roles.
• Experience with inference serving frameworks like TensorRT-LLM.
• Prior experience at a company with a forward-deployed engineering model (e.g., Palantir, Scale AI, OpenAI).
• Track record of taking GenAI POCs from prototype to production-scale.

Culture & Benefits

• Meaningful equity in a fast-growing, well-funded startup.
• Opportunity to work with bleeding-edge technology at the forefront of AI infrastructure.
• Collaborative environment with world-class researchers and engineers.
• Comprehensive benefits package.
• High-impact role with minimal bureaucracy.