Member of Technical Staff, AI

Job description

About the Role As a Member of Technical Staff focused on AI, you will design applied AI systems and build the products around them across frontend, backend, and data. End-to-end product ownership is central to the role. You will define the problem, decide what to build, ship it, evaluate the result, and maintain it after launch. Most of your technical work will involve LLM-powered applications, agent systems, and the evaluation and reliability systems required to run them in production. The role is full lifecycle • Find product-market fit. Start with a small version, test it with users, and decide whether to continue, change direction, or stop. • Improve what works. Once a product proves useful, test changes and invest in the versions that perform best. • Expand and scale. Grow adoption across WITHIN and with clients, and make the system reliable under increased use. • Maintain it. Monitor performance, resolve problems, and continue improving the product. You will set direction with business partners. They provide domain context and user needs; you decide how to scope, sequence, and develop the product. How we work • AI-native.  We use coding assistants and agent workflows throughout development. Engineers are responsible for testing and reviewing the resulting work. • Bias to ship.  Engineers work directly with business teams, make product and technical decisions, and release work in small increments. • Fast feedback loops, evidence over vibes.  We observe how products perform and use feedback and product data to decide what to build next. What we’re looking for How you operate matters more than any specific technology: • You have owned products end to end, from an ambiguous initial problem through launch and ongoing operation. • You can decide what to test first, define success, and change course when results do not support the current plan. • AI tools are already part of how you scope, build, test, and maintain software. • You can choose between model behavior, deterministic software, and human review based on the problem. • You work independently, communicate tradeoffs, and keep the people involved informed. Technical foundation: • Strong Python skills and experience building production systems. • Experience building production applications with LLM APIs such as OpenAI, Anthropic, or Vertex AI. • Experience building agent workflows that use tools, call external systems, maintain state, and handle multi-step tasks. • Experience designing evaluations for LLM-powered systems, including test cases, quality measures, regression testing, and review of production failures. • Understanding of context management, token limits, multi-turn conversations, tool calling, structured outputs, prompt design, and common failure modes. • Experience integrating external APIs and working with SQL. • Experience tracing and debugging behavior across model calls, application code, tools, and external systems. • Experience with GCP, AWS, or Azure. • Ability to build the product around the AI components, including frontend, backend, and data. Experience with TypeScript, React, and backend services is a plus. • Ability to explain technical tradeoffs and work directly with business teams. Strong pluses • • Multi-agent architectures. • Embeddings, vector search, and retrieval-augmented generation. • Stateful or sandboxed code execution environments. • Human review and approval workflows. • Observability, model routing, latency management, and cost control for AI systems. • Document automation (Google Docs, Slides, PDFs). • Building internal developer tools or productivity platforms. • Data warehouses (BigQuery, Snowflake, Databricks), data transformation (dbt), semantic layers (Cube, Looker, dbt Metrics). Our intervie