Mid-Level AI Engineer

Join a core product team building agentic systems that automate complex workflows across regulated and enterprise domains. You will ship production LLM-based services and help make them reliable, safe, and useful for customers.

• Design and maintain agentic systems for multi-step workflows across areas such as healthcare, legal, fintech, logistics, and compliance.
• Build production retrieval-augmented generation pipelines and retrieval infrastructure, including vector databases, embeddings, and indexing.
• Implement multi-agent orchestration, tool calling, memory, and reasoning components.
• Develop evaluation and safety tools to measure model performance, detect regressions, and support reliable production use.
• Deliver full-stack AI features, from APIs and data models through frontend and backend implementation, testing, deployment, and monitoring.
• Work with product and design partners to set priorities and success metrics, then improve features using user feedback and telemetry.

Base salary: $150,000 to $180,000 annually. Visa sponsorship is not available.

• Two to eight years of software engineering experience delivering user-facing or backend products, including production engineering experience in a product-focused environment.
• Practical experience deploying LLM services in production, including prompt design, orchestration, and tool integration.
• Proficiency with Python and TypeScript/React or equivalent, plus cloud platforms such as AWS or GCP and relational or NoSQL databases.
• Working knowledge of RAG, vector databases, embeddings, and retrieval pipelines.
• Experience building automated tests, evaluations, and monitoring for production AI systems, as well as API-driven, high-throughput systems and real-time features.
• Familiarity with agent or workflow frameworks such as LangGraph or CrewAI, and orchestration tools such as Temporal or Trigger.
• Experience with multi-tenant or enterprise systems, or regulated domains such as healthcare, fintech, or legal; familiarity with fine-tuning or multimodal models is also useful.
• A completed university degree and a track record of owning features from data model through deployment and monitoring.

On-site in San Francisco, California, United States.