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

• 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)

• 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

• 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

• 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

• 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

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.

• 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

• 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

• 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

• Competitive equity

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

• 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

• 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

1. What specifically about this company and this Solutions Architect role is exciting to you?
2. Probe: Did they do research? Is it the AI space, the stage, the technical challenge?
3. 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?
4. Are you able to work in San Mateo, CA or New York, NY and come into the office?
5. 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?
6. What's your hands-on experience with LLM inference frameworks like vLLM or SGLang and fine-tuning workflows like SFT or DPO?
7. What is your salary expectation?
8. How actively are you exploring new opportunities?
   

AI-Native Inference, MLOps & LLM Infrastructure Companies

(Highest-priority talent pool — candidates have deep open-model and serving framework experience)

• Together AI
• Replicated
• Modal Labs
• Baseten
• Anyscale
• OctoAI
• Groq
• Cerebras
• Mistral AI
• Cohere

(Ideally source candidates from above campanies)

Hyperscaler AI Platforms & Cloud Infrastructure

• Microsoft
• Google
• Amazon Web Services (AWS)
• NVIDIA
• AMD
• Databricks
• Snowflake
• MongoDB

(Ideally source candidates from above campanies)

AI-Native Developer Tools & Production AI Application Companies

• Cursor
• Notion
• Scale AI
• Weights & Biases
• Hugging Face
• LangChain
• Pinecone
• Weaviate
• Glean
• Perplexity AI

(Ideally source candidates from above campanies)

Pure Closed-Model API Wrapper Companies

• OpenAI
• Anthropic
• Jasper
• Copy.ai
• WRITER
• Typeface

(Don't source candidates from the above companies )

• Salesforce
• ServiceNow
• Workday Peakon Employee Voice
• SAP
• Oracle
• HubSpot
• Zendesk

(Don't source candidates from the above companies )

Team Size: 181 Employees

Industry: AI, API SDK, Devtools, Enterprise, Finance, Healthcare, Software Development

Founded: 2021

Total Funding: $327M

Office Locations: San Mateo, CA 1

Company Locations: San Mateo, CA 2