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
Traits to Avoid
LLM experience is limited to closed-model API wrappers with no exposure to open-model inference, serving frameworks, or fine-tuning
Pure advisory/consultant profiles without shipping production code
Pure Big Tech backgrounds with no startup or fast-paced field engineering exposure
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
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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
Candidate Screening Questions
What specifically about this company and this Solutions Architect role is exciting to you?
Probe: Did they do research? Is it the AI space, the stage, the technical challenge?
How do you partner with a sales representative to strategize for a large deal? Can you give an example of how you influenced the sales strategy?
Are you able to work in San Mateo, CA or New York, NY and come into the office?
Walk me through a time you built and shipped a POC or production integration directly inside a customer's environment — what was the stack and what did you own end-to-end?
What's your hands-on experience with LLM inference frameworks like vLLM or SGLang and fine-tuning workflows like SFT or DPO?
What is your salary expectation?
How actively are you exploring new opportunities?
