AI Field Engineer – Enterprise

Compensation: $176k – $224k

AI Field Engineer – Enterprise

About the Role

We're looking for an AI Field Engineer – Enterprise to help enterprise customers bring advanced AI models into production. This role combines hands-on AI engineering, technical problem-solving, and customer collaboration.

You'll work closely with enterprise customers and internal engineering and sales teams to understand complex requirements, build proof-of-concepts, optimize model inference, and deliver reliable AI solutions in real-world production environments.

This is an opportunity for an engineer who enjoys working at the intersection of open-source LLMs, AI infrastructure, and enterprise applications.

What You'll Do

• Partner with enterprise customers to understand technical requirements, identify use cases, and design AI implementation strategies.
• Build and deploy proof-of-concepts and production integrations directly within customer environments.
• Implement and optimize LLM inference and serving workflows using technologies such as vLLM, SGLang, and TensorRT-LLM.
• Work with open-source language models, fine-tuning workflows, and model evaluation techniques.
• Diagnose performance bottlenecks and improve inference latency, throughput, GPU utilization, and deployment efficiency.
• Develop production-quality Python code, APIs, integrations, and technical prototypes.
• Collaborate with sales teams on technical discovery, solution design, and enterprise deal strategy.
• Present technical solutions and architecture recommendations to engineers, technical leaders, and executive stakeholders.
• Translate customer feedback into actionable insights for internal product and engineering teams.
• Support customers throughout technical validation, deployment, and production adoption.

What We're Looking For

• 3+ years of relevant experience in AI/ML engineering, applied AI, solutions engineering, field engineering, or a related technical role.
• Strong Python programming and software engineering fundamentals.
• Hands-on experience building and deploying LLM-powered applications or AI systems in production.
• Experience with open-source LLM inference or serving frameworks such as vLLM, SGLang, or TensorRT-LLM.
• Familiarity with model fine-tuning approaches such as supervised fine-tuning (SFT); experience with DPO or reinforcement-based fine-tuning is a plus.
• Understanding of GPU-based workloads, inference performance, and cloud infrastructure.
• Experience with Docker, Kubernetes, and production deployment workflows.
• Ability to build technical POCs and take ownership of implementations from discovery through deployment.
• Strong communication skills and the ability to work directly with enterprise customers.
• Comfort collaborating with sales teams and translating technical capabilities into business value.

Preferred Qualifications

• Experience as a Forward Deployed Engineer, AI Solutions Engineer, Applied AI Engineer, or Solutions Architect.
• Experience deploying open-source models in enterprise environments.
• Familiarity with model evaluation, inference optimization, distributed serving, and GPU infrastructure.
• Experience working with enterprise technical stakeholders and complex customer requirements.

Location and Work Model

US-based opportunity with office hubs in San Mateo, California, and New York City.

The role is remote-friendly, with a hybrid schedule for employees near an office hub. Regular travel to enterprise customer locations is expected.

Candidates should be comfortable working directly with customers and supporting enterprise deployments.

Compensation

• Base salary: $176,000–$224,000 per year.
• Target on-target earnings (OTE): $220,000–$280,000 per year.
• Equity: Available as part of the compensation package.

Final compensation depends on experience, qualifications, location, and the overall offer structure.

Visa Sponsorship

H-1B transfers and TN visas are supported. O-1 sponsorship is considered on a case-by-case basis. Individual eligibility and sponsorship requirements will be evaluated during the hiring process.

Interview Process

The interview process may include an initial recruiter screen, a take-home exercise, a technical/culture interview, a discovery and hiring-manager discussion, an onsite final round, an executive interview, and a debrief.

Interested?

If you enjoy solving challenging AI infrastructure problems, building production-grade LLM applications, and working closely with enterprise customers, we'd like to hear from you.

Please share your resume and relevant examples of production AI deployments, open-source LLM serving, fine-tuning, or customer-facing engineering projects.