Software Engineer

Summary

Outmarket AI is an AI platform for insurance that automates complex workflows for brokerages. The company is seeking a Software Engineer to build AI-powered features and collaborate across various teams, focusing on enhancing customer workflows and product capabilities.

Responsibilities

• Build product features powered by LLMs, including retrieval-augmented generation (RAG), embeddings and semantic search, prompt design, structured output, and tool-using agents
• Work hands-on with the mechanics of AI systems: chunking and embedding documents, vector stores and retrieval, context construction, and evaluating output quality
• Build the backend services, data pipelines, and product UI that turn these AI capabilities into reliable, production-grade features
• Turn messy real-world insurance data into trustworthy, source-cited product capabilities
• Collaborate across product, sales, and customer success to ship high-leverage work

Skills

• 1 to 3 years of software engineering experience (or equivalent), with strong fundamentals
• Proficiency in Python and/or TypeScript and comfort with SQL
• A real understanding of how modern AI systems work under the hood: LLMs and prompting, tokens and context windows, embeddings and vector search, RAG, and basic evaluation of model output
• Genuine interest in applied AI and a desire to go deep on the domain
• Strong ownership and comfort in a fast-moving environment
• Hands-on experience with LLM orchestration and RAG tooling (for example LangChain, LlamaIndex, or similar) and vector databases (for example pgvector, Pinecone, or Weaviate)
• Experience building agents, tool/function calling, or evaluation and prompt-testing pipelines
• Experience with React, Postgres, or cloud platforms, or with document-heavy data

Qualifications

Must Haves

• 1 to 3 years of software engineering experience (or equivalent), with strong fundamentals
• Proficiency in Python and/or TypeScript and comfort with SQL
• A real understanding of how modern AI systems work under the hood: LLMs and prompting, tokens and context windows, embeddings and vector search, RAG, and basic evaluation of model output
• Genuine interest in applied AI and a desire to go deep on the domain
• Strong ownership and comfort in a fast-moving environment

Nice to Haves

• Hands-on experience with LLM orchestration and RAG tooling (for example LangChain, LlamaIndex, or similar) and vector databases (for example pgvector, Pinecone, or Weaviate)
• Experience building agents, tool/function calling, or evaluation and prompt-testing pipelines
• Experience with React, Postgres, or cloud platforms, or with document-heavy data

Benefits

• Competitive compensation and meaningful equity.
• Direct collaboration with founders and real users.
• Remote-first flexibility.
• Grow quickly with mentorship from founders and experienced engineers.