AI Field Engineer - Enterprise in San Mateo, CA

About the 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 will 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.

Compensation
Base Salary: $176K - $224K
OTE: $220K - $280K (variable paid quarterly based on individual and team performance)
Equity: Meaningful equity included on top of OTE
Visa Sponsorship: H-1B transfers and TN visas sponsored; O-1 considered case-by-case

Work Arrangement
Employment Type: Full-time
Work Mode: Hybrid (US-based, remote-friendly)
Location: San Mateo, CA or New York, NY
Travel: Regular on-site travel to enterprise customers required

Key Responsibilities
- 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

Mandatory Requirements
- 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 required; DPO and RFT strong plus)
- 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

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

Interview Process
Recruiter Screen (30 minutes); Take-Home Assignment (self-paced); Culture + Live Coding (1 hour); Discovery + Hiring Manager (45 minutes); On-Site Final Loop (~2 hours); Executive Interview (30 minutes); Debrief (60 minutes); Pre-Offer (60 minutes)

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