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.
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