Location: San Mateo, CA or New York, NY
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
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
Deal Breakers
LLM experience 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
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)