AI Field Engineer Enterprise

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 customers 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. Youll 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 customers environment not just advisory work; youve built and deployed POCs/MVPs that ran in someone elses 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