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.