Harper
AI Platform Engineer - Member of Technical Staff
Harper, San Francisco, California, United States, 94199
The Mission
We're building an AI-powered insurance brokerage that's transforming the $900 billion commercial insurance market by automating processes that currently run on pre-internet systems. Fresh off our $8M seed round, we're looking for an exceptional AI Platform Engineer who can architect and develop the core infrastructure that powers our entire AI ecosystem. You'll build the foundational platform that enables our AI agents to operate across growth, sales, operations, and customer service. This includes extending our proprietary AI Grid context engineering system, developing evaluation infrastructure, building ML models for market-making and reasoning, and creating the systems that enable massive operational leverage (enabling one person to do the work of thousands). You'll be responsible for both ambient agents (background processes with context/memory) and the core systems that enable frontier agents (human-AI interfaces) to deliver exceptional experiences. We're committed to "Staying REAL" with our AI systems - building agents that are Reliable, Experience-focused, Accurate, and have Low latency. You will work directly with the CEO and CTO to execute on our AI vision with a bias toward action. We live by core principles: "There is no try, there is just do," "Actions lead to information, always default to action," and "Strong opinions lead to information." We need engineers who build and ship, not just plan and strategize. Outcomes You'll Drive
Extend and enhance our proprietary AI Grid context engineering system that combines ETL with LLM pipelines and graphs Build robust evaluation systems with datasets and human-annotated data for supervised fine-tuning (SFT) and reinforcement learning (RL) Develop ML models for critical systems including underwriter load balancing (our market-making engine) and reasoning systems Architect and maintain data pipelines that pull from multiple diverse sources and push to various destination systems (ClickHouse, vector databases, ML platforms, etc.) Build MCP (Model Context Protocol) servers that expose memory and tools to AI agents across the platform Create integrations with payment providers and financial systems for seamless transaction processing Develop growth engineering infrastructure including agents that determine campaign strategies and optimize outreach Build voice AI systems for follow-ups, information collection, and cold outbound campaigns Create customer service AI infrastructure enabling one person to manage thousands of leads and customers Design ETL/ELT pipelines that handle both batch and real-time processing at scale Implement Lambda architecture patterns combining event streaming with batch processing Partner with forward deployed engineers to ensure platform capabilities meet business needs Build comprehensive observability systems to monitor agent reliability and performance You're Our Person If
You're exceptional at one or more of: distributed systems, AI agents, context engineering, or data engineering You have experience building evaluation systems and working with human-annotated datasets for ML training You understand how to build ML models for complex systems like market-making and reasoning engines You have deep expertise with modern data warehouse and ML platforms (Databricks, Astronomer, or similar) You can architect MCP servers and understand how to expose memory and tools to AI systems You have experience with payment provider integrations and financial systems You understand how to build voice AI infrastructure for outbound campaigns and information collection You can architect systems that enable massive operational leverage (1:1000s ratios) You have experience with data enrichment and building DAG-based orchestration systems You understand CAP theorem tradeoffs and can make appropriate architectural decisions You're equally comfortable with TypeScript/Node.js and Python/ML frameworks You ship features daily and take immediate action instead of overthinking You embrace "there is no try, there is just do" as your engineering mantra Hard Requirements
Strong experience with both TypeScript/Node.js and Python Deep understanding of distributed systems principles, CAP theorem tradeoffs, and event sourcing architecture Experience with modern data warehouse solutions (Databricks, Astronomer, Snowflake, or similar) Proven track record building ML modeling infrastructure and ETL/ELT pipelines Experience with evaluation systems and human-annotated datasets for ML training Experience building ML models for complex systems (recommendation engines, market-making, reasoning) Experience with MCP server architecture and protocol implementation Experience with voice AI systems and conversational interfaces Experience with payment provider integrations and financial systems Experience designing data pipelines that serve multiple destination systems Experience with
temporal.io
workflows or similar durable execution frameworks Experience with data enrichment and DAG-based orchestration Proven track record building production AI/ML systems at scale Experience with vector databases (Qdrant, Pinecone, Weaviate) and RAG systems Strong understanding of context engineering for AI systems Advanced usage of Cursor or WindSurf coding IDE Must be based in San Francisco and work in-office 5.5 days per week (relocation assistance provided) Our Tech Stack
AI Agent Infrastructure:
AI Grid - our proprietary context engineering system combining ETL with LLM pipelines and graphs Temporal.io
for durable workflow orchestration across agent systems Pydantic-AI for type-safe agent development with structured validation MCP servers for exposing memory and tools to AI agents Evaluation systems with human-annotated datasets for SFT and RL Event sourcing architecture with Redis streams and PostgreSQL Lambda architecture combining real-time event streams and batch processing RAG systems with rigorous evaluation frameworks Claude (Anthropic), GPT-4 (OpenAI), and select open source models Voice AI infrastructure for campaigns and customer interactions Logfire for comprehensive agent observability Data & ML Infrastructure:
Modern data warehouse solutions (Databricks/Astronomer) for ML modeling and ETL ML models for underwriter load balancing (market-making) and reasoning systems Evaluation infrastructure with human-annotated datasets for SFT/RL Multiple destination systems including ClickHouse for analytics Apache Airflow, Temporal, Airbyte, and N8N for pipeline orchestration Vector databases for AI context storage and retrieval Custom data enrichment pipelines for growth engineering Payment provider integrations for transaction processing PostHog for product analytics and event tracking Redis streams and PostgreSQL for operational data Core Engineering:
TypeScript/Node.js for robust application development Python for AI systems and ML workflows Next.js/React for frontend experiences Event-driven architecture with distributed systems design What You'll Build in Your First 90 Days
First Month:
Extend and enhance our AI Grid context engineering system with new capabilities Build initial evaluation datasets and implement human annotation workflows Set up MCP servers for memory and tool exposure across AI agents Create voice AI infrastructure for outbound campaigns and information collection Design ML models for underwriter load balancing and reasoning systems Establish comprehensive observability and monitoring systems Second Month:
Develop sophisticated data enrichment pipelines for growth engineering Build agents that determine and optimize campaign strategies Implement payment provider integrations for seamless transactions Expand evaluation systems with automated dataset generation Create customer service AI infrastructure for 1:1000s operational leverage Implement advanced orchestration patterns for complex workflows Third Month:
Scale AI Grid to handle increasing complexity and volume Optimize ML models for market-making and reasoning performance Build comprehensive growth engineering platform with campaign automation Implement advanced voice AI features for cold outbound and follow-ups Fine-tune models using human-annotated datasets (SFT/RL) Integrate all systems into a unified, observable platform architecture Our AI Philosophy
Context is King : The quality of AI decisions directly correlates with the richness of available context through AI Grid 10x Platform Impact : Build infrastructure that enables forward deployed engineers to create 10x business leverage Evaluation-Driven Development : Use human-annotated data and rigorous evaluation to continuously improve Multi-System Integration : Design for multiple sources and destinations from day one Event-Driven Architecture : React to events and state changes for maximum responsiveness Distributed & Durable : Create fault-tolerant systems that maintain state and recover from failures Business-Enabling Infrastructure : Every platform capability should unlock new business opportunities Action Orientation : Always default to action - ship code, gather data, and iterate Execution Focus : There is no try, there is just do - we value engineers who build and ship Join Us To Transform the $900B Insurance Market
This is an early-stage role at a fast-moving startup, and you'll often experience the crawl-walk-run approach to building. You'll quickly prototype systems and then push them into productionized platforms that can scale. We're looking for people who can be creative in providing impact first, then take learnings from that impact and push them back into the system. You should ideally have worked in an early-stage startup environment and understand the pacing. This is a fast-paced environment where we value ownership and quick, rapid feedback loops within the team. You'll work directly with the CEO and CTO to execute on our AI vision with a bias toward action. We require you to be in San Francisco and work from our office 5.5 days per week. We'll cover relocation costs and believe the best teams collaborate intensively in person. Skills
TypeScript, Node.js, Python, Distributed Systems, Context Engineering, AI Grid, Data Engineering, Databricks, Astronomer, ETL/ELT, ML Infrastructure, Data Enrichment, DAG Orchestration,
Temporal.io , Pydantic-AI, MCP Servers, Evaluation Systems, SFT/RL, Voice AI, Payment Integration, Event Sourcing, CAP Theorem, Lambda Architecture, Apache Airflow, Vector Databases, RAG Systems, Redis Streams, PostgreSQL, AI Agent Development, System Architecture, Market-Making Systems, Reasoning Engines
#J-18808-Ljbffr
We're building an AI-powered insurance brokerage that's transforming the $900 billion commercial insurance market by automating processes that currently run on pre-internet systems. Fresh off our $8M seed round, we're looking for an exceptional AI Platform Engineer who can architect and develop the core infrastructure that powers our entire AI ecosystem. You'll build the foundational platform that enables our AI agents to operate across growth, sales, operations, and customer service. This includes extending our proprietary AI Grid context engineering system, developing evaluation infrastructure, building ML models for market-making and reasoning, and creating the systems that enable massive operational leverage (enabling one person to do the work of thousands). You'll be responsible for both ambient agents (background processes with context/memory) and the core systems that enable frontier agents (human-AI interfaces) to deliver exceptional experiences. We're committed to "Staying REAL" with our AI systems - building agents that are Reliable, Experience-focused, Accurate, and have Low latency. You will work directly with the CEO and CTO to execute on our AI vision with a bias toward action. We live by core principles: "There is no try, there is just do," "Actions lead to information, always default to action," and "Strong opinions lead to information." We need engineers who build and ship, not just plan and strategize. Outcomes You'll Drive
Extend and enhance our proprietary AI Grid context engineering system that combines ETL with LLM pipelines and graphs Build robust evaluation systems with datasets and human-annotated data for supervised fine-tuning (SFT) and reinforcement learning (RL) Develop ML models for critical systems including underwriter load balancing (our market-making engine) and reasoning systems Architect and maintain data pipelines that pull from multiple diverse sources and push to various destination systems (ClickHouse, vector databases, ML platforms, etc.) Build MCP (Model Context Protocol) servers that expose memory and tools to AI agents across the platform Create integrations with payment providers and financial systems for seamless transaction processing Develop growth engineering infrastructure including agents that determine campaign strategies and optimize outreach Build voice AI systems for follow-ups, information collection, and cold outbound campaigns Create customer service AI infrastructure enabling one person to manage thousands of leads and customers Design ETL/ELT pipelines that handle both batch and real-time processing at scale Implement Lambda architecture patterns combining event streaming with batch processing Partner with forward deployed engineers to ensure platform capabilities meet business needs Build comprehensive observability systems to monitor agent reliability and performance You're Our Person If
You're exceptional at one or more of: distributed systems, AI agents, context engineering, or data engineering You have experience building evaluation systems and working with human-annotated datasets for ML training You understand how to build ML models for complex systems like market-making and reasoning engines You have deep expertise with modern data warehouse and ML platforms (Databricks, Astronomer, or similar) You can architect MCP servers and understand how to expose memory and tools to AI systems You have experience with payment provider integrations and financial systems You understand how to build voice AI infrastructure for outbound campaigns and information collection You can architect systems that enable massive operational leverage (1:1000s ratios) You have experience with data enrichment and building DAG-based orchestration systems You understand CAP theorem tradeoffs and can make appropriate architectural decisions You're equally comfortable with TypeScript/Node.js and Python/ML frameworks You ship features daily and take immediate action instead of overthinking You embrace "there is no try, there is just do" as your engineering mantra Hard Requirements
Strong experience with both TypeScript/Node.js and Python Deep understanding of distributed systems principles, CAP theorem tradeoffs, and event sourcing architecture Experience with modern data warehouse solutions (Databricks, Astronomer, Snowflake, or similar) Proven track record building ML modeling infrastructure and ETL/ELT pipelines Experience with evaluation systems and human-annotated datasets for ML training Experience building ML models for complex systems (recommendation engines, market-making, reasoning) Experience with MCP server architecture and protocol implementation Experience with voice AI systems and conversational interfaces Experience with payment provider integrations and financial systems Experience designing data pipelines that serve multiple destination systems Experience with
temporal.io
workflows or similar durable execution frameworks Experience with data enrichment and DAG-based orchestration Proven track record building production AI/ML systems at scale Experience with vector databases (Qdrant, Pinecone, Weaviate) and RAG systems Strong understanding of context engineering for AI systems Advanced usage of Cursor or WindSurf coding IDE Must be based in San Francisco and work in-office 5.5 days per week (relocation assistance provided) Our Tech Stack
AI Agent Infrastructure:
AI Grid - our proprietary context engineering system combining ETL with LLM pipelines and graphs Temporal.io
for durable workflow orchestration across agent systems Pydantic-AI for type-safe agent development with structured validation MCP servers for exposing memory and tools to AI agents Evaluation systems with human-annotated datasets for SFT and RL Event sourcing architecture with Redis streams and PostgreSQL Lambda architecture combining real-time event streams and batch processing RAG systems with rigorous evaluation frameworks Claude (Anthropic), GPT-4 (OpenAI), and select open source models Voice AI infrastructure for campaigns and customer interactions Logfire for comprehensive agent observability Data & ML Infrastructure:
Modern data warehouse solutions (Databricks/Astronomer) for ML modeling and ETL ML models for underwriter load balancing (market-making) and reasoning systems Evaluation infrastructure with human-annotated datasets for SFT/RL Multiple destination systems including ClickHouse for analytics Apache Airflow, Temporal, Airbyte, and N8N for pipeline orchestration Vector databases for AI context storage and retrieval Custom data enrichment pipelines for growth engineering Payment provider integrations for transaction processing PostHog for product analytics and event tracking Redis streams and PostgreSQL for operational data Core Engineering:
TypeScript/Node.js for robust application development Python for AI systems and ML workflows Next.js/React for frontend experiences Event-driven architecture with distributed systems design What You'll Build in Your First 90 Days
First Month:
Extend and enhance our AI Grid context engineering system with new capabilities Build initial evaluation datasets and implement human annotation workflows Set up MCP servers for memory and tool exposure across AI agents Create voice AI infrastructure for outbound campaigns and information collection Design ML models for underwriter load balancing and reasoning systems Establish comprehensive observability and monitoring systems Second Month:
Develop sophisticated data enrichment pipelines for growth engineering Build agents that determine and optimize campaign strategies Implement payment provider integrations for seamless transactions Expand evaluation systems with automated dataset generation Create customer service AI infrastructure for 1:1000s operational leverage Implement advanced orchestration patterns for complex workflows Third Month:
Scale AI Grid to handle increasing complexity and volume Optimize ML models for market-making and reasoning performance Build comprehensive growth engineering platform with campaign automation Implement advanced voice AI features for cold outbound and follow-ups Fine-tune models using human-annotated datasets (SFT/RL) Integrate all systems into a unified, observable platform architecture Our AI Philosophy
Context is King : The quality of AI decisions directly correlates with the richness of available context through AI Grid 10x Platform Impact : Build infrastructure that enables forward deployed engineers to create 10x business leverage Evaluation-Driven Development : Use human-annotated data and rigorous evaluation to continuously improve Multi-System Integration : Design for multiple sources and destinations from day one Event-Driven Architecture : React to events and state changes for maximum responsiveness Distributed & Durable : Create fault-tolerant systems that maintain state and recover from failures Business-Enabling Infrastructure : Every platform capability should unlock new business opportunities Action Orientation : Always default to action - ship code, gather data, and iterate Execution Focus : There is no try, there is just do - we value engineers who build and ship Join Us To Transform the $900B Insurance Market
This is an early-stage role at a fast-moving startup, and you'll often experience the crawl-walk-run approach to building. You'll quickly prototype systems and then push them into productionized platforms that can scale. We're looking for people who can be creative in providing impact first, then take learnings from that impact and push them back into the system. You should ideally have worked in an early-stage startup environment and understand the pacing. This is a fast-paced environment where we value ownership and quick, rapid feedback loops within the team. You'll work directly with the CEO and CTO to execute on our AI vision with a bias toward action. We require you to be in San Francisco and work from our office 5.5 days per week. We'll cover relocation costs and believe the best teams collaborate intensively in person. Skills
TypeScript, Node.js, Python, Distributed Systems, Context Engineering, AI Grid, Data Engineering, Databricks, Astronomer, ETL/ELT, ML Infrastructure, Data Enrichment, DAG Orchestration,
Temporal.io , Pydantic-AI, MCP Servers, Evaluation Systems, SFT/RL, Voice AI, Payment Integration, Event Sourcing, CAP Theorem, Lambda Architecture, Apache Airflow, Vector Databases, RAG Systems, Redis Streams, PostgreSQL, AI Agent Development, System Architecture, Market-Making Systems, Reasoning Engines
#J-18808-Ljbffr