AI Field Engineer - AI Natives

AI Field Engineer – AI Natives

Location:

New York, NY / San Mateo, CA / Remote, USA

US-based role with the option to work remotely or from the New York or San Mateo office.

Compensation: $176,000 – $228,000 OTE (80% Base / 20% Variable) + Meaningful Equity

Visa:
Open to H-1B transfers and TN visa sponsorship. O-1 sponsorship considered on a case-by-case basis.

Company Stage:
Growth-Stage / Series C AI Infrastructure Company

Industry: Artificial Intelligence, Generative AI, Machine Learning, AI Infrastructure, LLMs, Developer Infrastructure, Enterprise Software, B2B SaaS, Cloud Computing, GPU Infrastructure

About the Company:

Our client is building a high-performance AI infrastructure platform that enables companies to build, tune, and scale production AI applications using open models. The platform provides production-grade inference infrastructure, model serving, fine-tuning, evaluation capabilities, and enterprise integrations designed to help AI-native companies move from experimentation to production quickly. The company powers production AI workloads for leading technology companies and AI-native organizations, supporting demanding use cases that require high performance, low latency, reliability, and significant inference scale.

The company was founded by engineers and AI infrastructure leaders from some of the world's leading technology organizations and has built a highly technical culture centered around open models, production AI systems, and rapid innovation. The AI Field Engineering team is a small, high-velocity group focused specifically on working with ambitious AI-native customers. The team operates with extreme ownership and expects engineers to combine deep technical expertise with strong customer-facing and product instincts.

As an AI Field Engineer, you'll embed directly with AI-native customers to solve complex AI infrastructure problems, build proof-of-concepts and production integrations, and help customers move rapidly from prototype to production. This is a highly technical, customer-facing engineering role where you'll operate at the intersection of engineering, product, and customer delivery.

What You'll Do:

• Work directly with ambitious AI-native customers to solve complex AI infrastructure and application problems
• Act as a technical partner to customers from initial discovery through production deployment
• Build proof-of-concepts, MVPs, and production AI integrations
• Design and implement production-grade AI systems using open-source models
• Work hands-on with LLM inference, model serving, fine-tuning, and AI application infrastructure
• Help customers evaluate and deploy open models for production workloads
• Develop customer-specific technical solutions across inference, model serving, fine-tuning, and deployment
• Work closely with customer engineering teams to understand their architecture and technical requirements
• Translate complex customer requirements into reliable technical solutions
• Participate in executive-level customer conversations around architecture, strategy, and business outcomes
• Explain highly technical AI infrastructure concepts clearly to engineering and executive stakeholders
• Partner with account executives and customer engineering teams throughout technical engagements
• Own customer technical projects from initial scoping through implementation and production deployment
• Build and ship production-quality code rather than operating solely in an advisory capacity
• Contribute directly to internal codebases and platform improvements
• Translate customer feedback and field learnings into concrete product improvements
• Work closely with product and engineering teams to influence platform direction
• Identify recurring customer problems and develop reusable technical solutions
• Build integrations that can scale across multiple customers and use cases
• Work with modern LLM serving frameworks such as vLLM, SGLang, and TensorRT-LLM
• Work with GPU infrastructure and high-performance AI workloads
• Design and optimize inference systems for latency, throughput, reliability, and cost
• Work with Kubernetes and modern cloud infrastructure
• Deploy AI systems across AWS, Azure, and GCP environments
• Work with…