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David Careers

AI Field Engineer - AI Natives

Posted 12 days ago

recruiting from scratchSan Mateo (CA)
Computer Systems Engineers/ArchitectsCustom Computer Programming Services

SENIORITY

Lead

SALARY

$176,000 – $228,000

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About the role

AI Field Engineer – AI Natives
Location: New York, NY / San Mateo, CA / Remote, USAUS-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 deploymentBuild 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 deploymentBuild 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-LLMWork 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 enterprise AI platforms including AWS Bedrock, AWS SageMaker, GCP Vertex AI, and Azure AI Foundry
  • Support customers implementing model fine-tuning workflows including SFT, DPO, and RFTHelp customers develop evaluation strategies for production AI systems
  • Work with customers to understand model performance and production AI quality requirements
  • Help customers move AI workloads from experimentation into production environments
  • Rapidly learn new customer architectures, technical environments, and AI use cases
  • Operate across engineering, product, infrastructure, and customer-facing responsibilities
  • Work on multiple high-priority customer engagements in parallel
  • Context-switch rapidly between different technical problems and customer environments
  • Operate with high urgency and strong ownership in a fast-moving AI company
  • Make independent technical decisions in ambiguous situations
  • Become client-facing within the first weeks of joining the team
  • Take responsibility for customer outcomes rather than simply providing technical recommendations
  • Help establish repeatable field engineering patterns and technical deployment practices
  • Contribute to the long-term product and technical strategy through customer insights
  • Ideal Candidate Background:
  • Experience Requirements4–10+ years of experience in software engineering, AI/ML engineering, applied AI, solutions engineering, forward deployed engineering, or similar technical roles preferred
Strong software engineering background with significant hands-on coding experienceExperience building and deploying AI/ML systems in productionExperience working with LLMs and generative AI systemsExperience building AI-powered applications or infrastructureExperience working directly with customers, clients, or technical stakeholders preferredExperience owning technical projects from discovery through productionExperience operating in fast-moving, ambiguous environments Experience working across engineering, product, and customer-facing responsibilities Strong experience with Python Experience with modern cloud infrastructure and distributed systemsExperience working with open-source models or AI infrastructure preferredExperience working with AI inference or model serving preferredExperience with production AI workloads preferredExperience at AI-native startups, high-growth technology companies, or similarly demanding environments preferred Demonstrated ability to independently solve complex technical problems Strong ownership mentality with a track record of shipping meaningful technical projects Ability to move quickly from customer problem to working technical solution Experience communicating technical concepts to both engineers and business stakeholders Strong builder mentality and willingness to work outside narrowly defined engineering responsibilities Technical Requirements Strong software engineering fundamentals Strong Python development experienceExperience building production AI/ML systems Strong understanding of LLMs and generative AIExperience working with open-source AI models preferredExperience with LLM inference and model serving Familiarity with vLLM, SGLang, TensorRT-LLM, or similar inference frameworks Experience with GPU infrastructure and accelerated computing preferredUnderstanding of model serving architecture and inference optimizationExperience with Kubernetes or similar container orchestration platforms Strong cloud infrastructure experienceExperience with AWS, Azure, GCP, or similar cloud platforms Familiarity with AWS Bedrock, AWS SageMaker, GCP Vertex AI, or Azure AI Foundry preferredExperience designing and deploying scalable AI systems Understanding of latency, throughput, concurrency, reliability, and cost optimizationExperience working with distributed systems and production infrastructureExperience with APIs and backend application architecture Experience building production-grade AI integrations Familiarity with LLM fine-tuning workflows such as SFT, DPO, or RFT preferredUnderstanding of AI evaluation and model performance measurement Experience working with production AI observability and reliability considerations preferred Ability to debug complex AI and infrastructure systems Ability to understand unfamiliar technical architectures quickly Ability to rapidly prototype and iterate on technical solutions Strong system design and technical architecture capabilities Ability to translate ambiguous customer requirements into production

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