AI Field Engineer

Job Description

AI Field Engineer - Enterprise

Employment Type: Full-time

Work Mode: Hybrid (US-based, remote-friendly)

Location: San Mateo, CA / New York, NY

Compensation: $176K - $224K Base (OTE: $220K - $280K)

Seniority: 3+ Years Experience

Seniority

• 3+ years of experience in customer-facing AI/ML field engineering (FDE, Applied AI, Solutions Architect, AI Infra, ML Engineer, Software Engineer with pre-sales exposure, or research backgrounds transitioning to customer-facing roles)

Work Experience

• Shipped AI/ML production code inside a customer's environment
• Hands-on LLM inference and fine-tuning experience — ran SFT pipelines, benchmarked latency, and tuned open-model deployments
• Ran the full field cycle in a pre-sales or customer-facing capacity — discovery, POC scoping, load tests, evals, and model selection
• Background at an AI-native/AI-infra startup (inference, MLOps, developer tooling) or enterprise SaaS with built-in AI features

Hard Skills

• LLM serving frameworks (vLLM, SGLang, TensorRT-LLM), agents, inference trade-offs, terminal-comfortable
• Python and Kubernetes proficiency
• Trained open models and familiar with fine-tuning methodologies (SFT, DPO, RFT)
• GPU optimization for LLM workloads

Soft Skills

• Demonstrated executive presence in enterprise customer-facing roles
• Navigated enterprise org politics end-to-end — champions, detractors, security reviews, and procurement cycles
• Miscellaneous
• Domestic travel to enterprise customers as needed

About This Role

We are looking for an AI Field Engineer (Enterprise) with 3+ years of experience to embed with enterprise customers and turn complex GenAI challenges into production systems — fast. You'll be the technical tip of the spear, pairing deep hands-on engineering with the executive presence to earn trust across large organizations and drive deals from first discovery call to production deployment.

What Will You Be Doing?

• Lead technical discovery calls, scope POCs, and run load tests and evaluations to validate the right model architecture and deployment configuration for each enterprise customer
• Build end-to-end POCs and production integrations hands-on-keyboard inside customer environments, navigating their infrastructure, security requirements, and organizational constraints
• Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation frameworks — moving them from open-model exploration to production at scale
• Manage multi-stakeholder enterprise relationships — identifying technical champions, navigating org politics, and aligning the right people to move deals forward quickly
• Feed recurring customer pain points and deployment patterns back into the product roadmap, acting as a direct feedback loop between the field and engineering

Requirements

Key Requirements

• Deep hands-on experience with LLM inference and/or training — working knowledge of open-model frameworks (vLLM, SGLang, TensorRT-LLM) and fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus); candidates with only closed-model/API-wrapper experience will not clear the bar
• Proven ability to ship production code inside a customer's environment — not just advisory work; you've built and deployed POCs/MVPs that ran in someone else's prod system
• Strong Python skills plus GPU/cloud infrastructure experience (AWS, Azure, or GCP) and comfort with Kubernetes
• Executive presence and enterprise navigation skills — able to run a technical deep-dive with an ML engineer and present architecture trade-offs to a VP in the same afternoon
• Pre-sales or customer-facing field engineering experience (FDE, Applied AI Engineer, Solutions Architect, or similar); pure software engineers without customer-facing exposure are not a fit

Compensation & Benefits

• Salary
• $176K - $224K Base
• OTE: $220K - $280K
• Variable component paid quarterly based on individual and team performance
• Compensation scales with experience
• Candidates with 10+ years may be considered for above-range packages
• Meaningful equity included on top of OTE

Equity

• Competitive equity

Visa Sponsorship

• H-1B transfers and TN visas sponsored
• O-1 considered on a case-by-case basis

Remote Work Policy

• US-based, remote-friendly
• Offices in San Mateo, CA and New York, NY
• Role requires regular on-site travel to enterprise customers
• Hybrid policy (Mon/Wed/Fri in-office) applies for those based near a hub

Tech Stack

• Python
• vLLM
• SGLang
• TensorRT-LLM
• Kubernetes
• AWS
• Azure
• GCP
• Azure AI Foundry
• AWS Bedrock
• AWS SageMaker
• GCP Vertex AI
• LLM Fine-Tuning (SFT, DPO, RFT)
• GPU Infrastructure
• Open-source LLM frameworks