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AI Field Engineer - GenAI Infrastructure

Stealth • United State
Remote Visa Sponsorship
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AI Summary

We are seeking an AI Field Engineer to help enterprises deploy open models in production. The ideal candidate will have hands-on experience with LLM inference and training, as well as strong Python and GPU/cloud infrastructure skills. The role involves scoping POCs, running load tests, and shipping production integrations directly into customer infrastructure.

Key Highlights
Hands-on experience with LLM inference and training
Strong Python and GPU/cloud infrastructure skills
Experience scoping POCs, running load tests, and shipping production integrations
Key Responsibilities
Scope POCs and run load tests
Fine-tune models and ship production integrations directly into customer infrastructure
Work hands-on-keyboard inside customer environments
Technical Skills Required
Python GPU/cloud infrastructure LLM inference and training
Benefits & Perks
Competitive salary ($176K-$224K base, $220K-$280K OTE)
Fully remote work arrangement
Visa sponsorship (H-1B transfers and TN sponsored, O-1 case-by-case)
Nice to Have
DPO/RFT fine-tuning experience
Background at a forward-deployed or professional-services shop
Product instincts and experience feeding field learnings back into a roadmap

Job Description


AI Field Engineer

Stealth GenAI Infra Platform


Remote (US) | $176K - $224K (OTE: $220K - $280K)


I'm hiring an AI Field Engineer for a fast-growing, AI-native infrastructure company building the fastest inference platform for GenAI on open models, already powering production workloads at companies like Uber, DoorDash, Notion, and Cursor, at 15x the speed and 4x the concurrency of closed-model alternatives.


The problem you'd help solve: enterprises are stuck exploring open models in a sandbox instead of running them in production. You're the person who closes that gap — hands-on-keyboard, inside their environment, from first discovery call to live deployment.


This role isn't advisory. You'll scope POCs, run load tests, fine-tune models (SFT/DPO/RFT), and ship production integrations directly into customer infrastructure — not hand off a slide deck and walk away.


You'll likely be a fit if you have:

  • 3+ years in customer-facing AI/ML field engineering (FDE, Applied AI Engineer, Solutions Architect, AI Infra/ML Engineer with pre-sales exposure)
  • Deep hands-on LLM inference and/or training experience — working knowledge of vLLM, SGLang, or TensorRT-LLM, plus fine-tuning workflows (SFT minimum)
  • Strong Python, plus GPU/cloud infra experience (AWS, Azure, or GCP) and comfort with Kubernetes
  • A track record of shipping production code inside a customer's environment — not just advisory work
  • Executive presence — you can run a technical deep-dive with an ML engineer and brief a VP on architecture trade-offs the same afternoon


Nice to have:

  • DPO/RFT fine-tuning experience
  • Background at a forward-deployed or professional-services shop (Palantir FDE, BCG X, McKinsey QuantumBlack)
  • Product instincts — you've fed field learnings back into a roadmap, not just filed tickets


What you won't find here:

This isn't for you if your experience is closed-model/API-wrapper only, or if you've never shipped in someone else's prod system — the team screens hard for that.


  • Package: $176K–$224K base / $220K–$280K OTE (80/20 split), scaling above range for 10+ years' experience
  • Competitive equity
  • Fully remote (US), regular on-site travel to enterprise customers; hybrid for those near a hub
  • Visa sponsorship: H-1B transfers and TN sponsored, O-1 case-by-case

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