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) Career Path Recruitment is running this search on behalf of the hiring company.