AI Field Engineer - Enterprise

Cross Border Talents; San Mateo (CA); Hybrid; USD 176,000 - 224,000; Full time

Job summary

Cross Border Talents is seeking an AI Field Engineer (Enterprise) to embed with major enterprise customers from San Mateo to New York. You will lead technical discovery, scope POCs, run load tests, and oversee production deployments of GenAI solutions, pairing hands-on engineering with executive-level communication.

The role requires 3+ years in customer-facing AI/ML, hands-on LLM inference and fine-tuning, Python and Kubernetes, and open-model experience.

Qualifications

• 3+ years in customer-facing AI/ML field engineering.
• Shipped AI/ML production code inside a customer environment.
• Experience with LLM inference and fine-tuning pipelines.
• Experience with cloud infrastructure (AWS/Azure/GCP) and Kubernetes.
• Pre-sales or customer-facing field engineering experience.

Responsibilities

• Lead technical discovery calls, scope POCs, and run load tests and evaluations.
• Build end-to-end POCs and production integrations inside customer environments.
• Guide customers on model selection and fine-tuning strategies (SFT/DPO/RFT).
• Manage multi-stakeholder enterprise relationships and drive deals forward.
• Feedback into product roadmap from field deployments.

Skills

LLM frameworks

Python

Kubernetes

Cloud infrastructure

Executive presence

Pre-sales / customer-facing

Tools

vLLM

SGLang

TensorRT-LLM

AWS

Azure

GCP

Job description

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

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