AI Summary
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. The role requires deep hands-on experience with LLM inference and/or training, and proven ability to ship production code inside a customer's environment. The ideal candidate will have executive presence and enterprise navigation skills.
Key Highlights
Lead technical discovery calls
Build end-to-end POCs and production integrations
Manage multi-stakeholder enterprise relationships
Key Responsibilities
Lead technical discovery calls
Build end-to-end POCs and production integrations
Manage multi-stakeholder enterprise relationships
Guide customers on model selection, fine-tuning strategy, and evaluation frameworks
Feed recurring customer pain points and deployment patterns back into the product roadmap
Technical Skills Required
Benefits & Perks
$176K - $224K Base
OTE: $220K - $280K
Variable component paid quarterly based on individual and team performance
Meaningful equity included on top of OTE
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
Nice to Have
GPU optimization for LLM workloads
Soft Skills: Demonstrated executive presence in enterprise customer-facing roles
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
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
- 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
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- 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.
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
- 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
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- 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
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- 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
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