AI Field Engineer – Enterprise

Full-Time | Remote (U.S.)

Compensation: $176,000 – $224,000 Base
On-Target Earnings (OTE): $220,000 – $280,000 + Meaningful Equity

About the Role

We are looking for an AI Field Engineer (Enterprise) with 3+ years of experience to embed with ambitious enterprise customers and turn complex GenAI challenges into production systems—fast.

You'll combine deep hands-on engineering with the executive presence to earn trust across large organizations and help drive engagements from initial technical discovery through production deployment.

What You'll Do

• Lead technical discovery calls, scope proof-of-concepts, and run load tests and evaluations to validate the right model architecture and deployment configuration for enterprise customers.
• Build end-to-end POCs and production integrations directly inside customer environments while navigating infrastructure, security requirements, and organizational constraints.
• Guide customers on model selection, fine-tuning strategies (SFT, DPO, RFT), and evaluation frameworks to move from experimentation to production at scale.
• Manage relationships across multiple enterprise stakeholders, identifying technical champions and helping align teams to move projects forward efficiently.
• Provide recurring customer feedback and deployment insights to the engineering organization to help shape future product development.

Required Qualifications

• Deep hands-on experience with LLM inference and/or training.
• Working knowledge of open-model frameworks such as:
  • vLLM
  • SGLang
  • TensorRT-LLM
• Experience with fine-tuning workflows:
  • SFT required
  • DPO and/or RFT strongly preferred
• Candidates whose experience is limited to closed-model API integrations alone will not be a fit.
• Proven ability to build and deploy production code within customer environments, including POCs or MVPs running in production.
• Strong Python programming skills.
• Experience with GPU infrastructure.
• Experience using AWS, Azure, or GCP.
• Experience with Kubernetes.

• Strong communication skills with the ability to engage both technical and executive audiences.
• Customer-facing engineering experience in roles such as:
  • Field Engineer
  • Applied AI Engineer
  • Solutions Architect
  • AI Infrastructure Engineer
  • ML Engineer
  • Software Engineer with pre-sales exposure
• 3+ years of relevant experience.

Preferred Qualifications

• Experience with DPO or reinforcement fine-tuning (RFT).
• Experience deploying enterprise-scale GenAI applications.
• Experience working directly with production AI infrastructure.

• Interest in contributing to product direction based on customer feedback.
• Comfortable working in fast-paced, high-growth environments with significant ownership.

Technical Environment

• Python
• vLLM
• SGLang
• TensorRT-LLM
• Kubernetes
• AWS
• Azure
• GCP
• Azure AI Foundry
• AWS Bedrock
• AWS SageMaker
• GCP Vertex AI
• GPU Infrastructure
• Open-source LLM Frameworks
• LLM Fine-Tuning (SFT, DPO, RFT)

Compensation & Benefits

• Base Salary: $176,000 – $224,000
• On-Target Earnings: $220,000 – $280,000 (80/20 base/variable split)

• Quarterly variable compensation based on individual and team performance

• Compensation may exceed the posted range for candidates with extensive experience.
• Meaningful equity package.

Visa Sponsorship

Visa sponsorship is available.

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

Location

Remote (United States)

Employees located near company hubs may follow a hybrid schedule. Regular travel to enterprise customer sites is expected.

About the Company

This fast-growing AI infrastructure company helps organizations build, fine-tune, and scale production AI applications using open-source models. Its platform is designed to deliver high-performance inference, lower latency, and greater scalability for enterprise AI workloads.

Founded in 2021, the company has grown to approximately 211 employees and has raised over $327 million in funding. The engineering culture emphasizes technical excellence, ownership, rapid execution, and close collaboration between customer-facing teams and product engineering.

The organization values engineers who enjoy solving complex technical challenges, working directly with customers, and helping shape the future of AI infrastructure.