AI Field Engineer - AI Natives

Recruiting From Scratch•San Francisco, CA

5d•Remote

About The Position

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. You'll be expected to work directly with customers early in your tenure, own technical engagements end-to-end, contribute production code internally, and translate customer learnings into improvements to the broader platform.

Requirements

4–10+ 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 experience
Experience building and deploying AI/ML systems in production
Experience working with LLMs and generative AI systems
Experience building AI-powered applications or infrastructure
Experience working directly with customers, clients, or technical stakeholders preferred
Experience owning technical projects from discovery through production
Experience 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 systems
Experience working with open-source models or AI infrastructure preferred
Experience working with AI inference or model serving preferred
Experience with production AI workloads preferred
Experience 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
Strong software engineering fundamentals
Strong Python development experience
Experience building production AI/ML systems
Strong understanding of LLMs and generative AI
Experience working with open-source AI models preferred
Experience with LLM inference and model serving
Familiarity with vLLM, SGLang, TensorRT-LLM, or similar inference frameworks
Experience with GPU infrastructure and accelerated computing preferred
Understanding of model serving architecture and inference optimization
Experience with Kubernetes or similar container orchestration platforms
Strong cloud infrastructure experience
Experience with AWS, Azure, GCP, or similar cloud platforms
Familiarity with AWS Bedrock, AWS SageMaker, GCP Vertex AI, or Azure AI Foundry preferred
Experience designing and deploying scalable AI systems
Understanding of latency, throughput, concurrency, reliability, and cost optimization
Experience working with distributed systems and production infrastructure
Experience with APIs and backend application architecture
Experience building production-grade AI integrations
Familiarity with LLM fine-tuning workflows such as SFT, DPO, or RFT preferred
Understanding 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-ready systems
Strong understanding of software engineering best practices
Comfortable working across application, infrastructure, model, and deployment layers
Ability to make pragmatic technical trade-offs based on customer and business requirements
Strong technical judgment around customer-specific implementations versus reusable platform capabilities
Extremely high agency and ownership
Strong bias toward action
Highly curious and intellectually engaged
Comfortable operating independently
Strong builder mentality
Deep technical curiosity
Passionate about AI and emerging technologies
Comfortable working directly with customers
Strong customer empathy
Comfortable communicating with technical and executive stakeholders
Strong interpersonal and communication skills
Excellent problem-solving ability
Comfortable working through ambiguity
Fast learner
Strong technical judgment
High intensity and urgency
Comfortable taking responsibility for customer outcomes
Strong product intuition
Strong business judgment
Able to translate customer problems into technical solutions
Comfortable balancing customer requirements with scalable technical architecture
Comfortable switching rapidly between different technical problems
Comfortable working across engineering, product, and customer-facing responsibilities
Comfortable receiving direct customer feedback
Willing to iterate quickly based on customer needs
Low-ego and highly collaborative
Strong team orientation
Comfortable working with highly technical customers
Comfortable participating in executive-level conversations
Excited by working with AI-native companies
Excited by open-source AI models and modern AI infrastructure
Hungry to learn and build
Comfortable operating in a flat, high-trust environment
Comfortable with minimal hand-holding
Comfortable working in a small, high-velocity engineering organization
Strong sense of urgency and accountability
Excited by building new systems and solving novel AI problems

Nice To Haves

Experience working with open-source models or AI infrastructure preferred
Experience working with AI inference or model serving preferred
Experience with production AI workloads preferred
Experience at AI-native startups, high-growth technology companies, or similarly demanding environments preferred
Familiarity with AWS Bedrock, AWS SageMaker, GCP Vertex AI, or Azure AI Foundry preferred
Familiarity with LLM fine-tuning workflows such as SFT, DPO, or RFT preferred
Experience working with production AI observability and reliability considerations preferred
Bachelor's degree preferred
Computer Science, Engineering, Mathematics, or equivalent technical disciplines preferred
Strong technical or quantitative academic background preferred
Exceptional practical engineering experience can compensate for academic pedigree

Responsibilities

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 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 RFT
Help 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