Lead Engineer, AI Platform

Accountabilities:: Design, build, and own evaluation infrastructure, including CI/CD pipelines, scorers, datasets, and systems for assessing AI agents from individual tool calls through complete multi-turn conversations. Develop observability and diagnostic capabilities to identify exactly where quality issues occur across planning, execution, tool selection, and complex agent trajectories. Investigate failures across sophisticated AI workflows and turn findings into technical prototypes, improvements, or clearly defined priorities for AI engineering teams. Build and expand datasets through human annotation, AI-generated examples, and simulated conversations to increase evaluation coverage efficiently. Develop structured experimentation frameworks for prompts, models, and agent harnesses, including evaluation of new and open-source models against production baselines. Identify opportunities to improve AI system cost and latency through model selection, caching, routing, and other optimization strategies. Set the technical direction and manage day-to-day priorities for the AI Quality engineering team while remaining actively involved in hands-on development. Partner closely with AI Core engineering teams to ensure changes to AI products can be measured effectively and demonstrably improve quality. Establish engineering practices and evaluation approaches that support reliable, efficient, and scalable production AI systems. Requirements: 7+ years of experience building and shipping production software, ideally including LLM-powered agents capable of taking real actions within products. Experience working with complex, tool-using AI systems involving multiple tools, planning, orchestration, or sub-agents rather than only simple, single-turn assistants. Strong ability to demonstrate shipped software and explain how its effectiveness and reliability were measured. Experience with Ruby on Rails and/or Python, with the ability to become productive quickly in technologies that may be new to you. Experience building evaluation or observability infrastructure for ML/AI systems, including evaluation pipelines, scorers, dashboards, or CI/CD systems for evaluations. Familiarity with evaluation frameworks such as Braintrust, LangSmith, or similar tools. Experience designing datasets, annotation workflows, or labeling pipelines for machine learning or AI evaluation. Ability to learn quickly, experiment extensively, and use empirical results to guide technical decisions. Comfortable operating in a fast-paced environment with ambiguity and changing technical requirements. Strong technical leadership and people-management capabilities, with the ability to balance team leadership and hands-on engineering. Excellent English proficiency in spoken, written, and reading communication, equivalent to CEFR C2 / ILR 5. Strong alignment with a collaborative, ownership-oriented engineering culture. Benefits: Annual cash compensation of $170,000 USD, benchmarked to U.S. compensation levels regardless of location. Equity in the company, including ongoing refresh grants. 35 days of paid time off per year. Fully remote work environment. Significant flexibility and autonomy in how you organize your work. Twice-yearly company retreats in international destinations. Benefits supporting health, wellbeing, and professional development. Opportunity to lead and grow an AI Quality engineering team while remaining hands-on technically. Exposure to advanced AI agents, evaluation infrastructure, observability, experimentation, and production AI optimization. Opportunity to work with a globally distributed team across multiple countries and time zones.