Principal Software Engineer for Copilot Evals

Establish data contracts and extensible schemas for customer signals, workflows, evaluation cases, rubrics, graders, runs, results, attribution, and regression assets. Develop quality-attribution workflows that combine automated diagnosis with human review; route losses to the appropriate product, data, model, tool, orchestration, or evaluation owner; track remediation; add regression protection; and verify improvement in customer outcomes. Create observability and benchmarks for pipeline health, agent behavior, data quality, grader health, coverage, cost, latency, operational reliability, and regression detection across CADET workflows and tooling. Design safe human-in-the-loop gates for ambiguous data, sensitive customer evidence, taxonomy changes, evaluation publication, and consequential quality decisions. Mentor engineers across the team and raise engineering quality through clear interfaces, testing, security, operational discipline, and technical documentation. Bachelor’s Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Extensive experience designing and delivering large-scale distributed systems, data platforms, developer platforms, or AI infrastructure in production, with practical exposure to large language model technologies, agent systems, or AI/ML platforms. Demonstrated principal-level technical leadership across teams and organizational boundaries. Solid understanding of security, privacy, data governance, and responsible engineering for sensitive data. Excellent communication and collaboration skills with the ability to influence senior engineering, science, and product leaders. Experience building AI/ML platforms, LLM or agent systems, evaluation infrastructure, experimentation systems, or model pipelines. Experience with retrieval, tool-using agents, prompt and rubric management, human-in-the-loop systems, or automated diagnosis. Experience building data and workflow platforms that combine batch, streaming, unstructured, and human-generated inputs. Experience with RLEs, synthetic environments, post-training pipelines, or large-scale evaluation execution. Experience integrating customer feedback, product telemetry, and engineering work-management systems.