(Coding Research) Member of Technical Staff

What You'll Do

• Design and own evaluation frameworks for coding agents, including benchmark specifications, scoring methodologies, rubrics, and quality standards.
• Lead end-to-end research initiatives focused on measuring and improving coding model performance across diverse software engineering tasks.
• Develop high-quality datasets, golden examples, and evaluation protocols that enable reliable assessment of frontier coding systems.
• Analyze model behavior and failure modes, identifying systematic weaknesses and translating findings into actionable improvements for training and evaluation.
• Build tooling and infrastructure that support large-scale experimentation, data generation, review workflows, and evaluation pipelines.
• Establish best practices for coding-agent assessment, ensuring methodological rigor, reproducibility, and measurement quality.
• Partner closely with researchers, engineers, and applied AI teams to design experiments and evaluate emerging model capabilities.
• Contribute to technical reports, benchmark studies, and client-facing research initiatives that communicate model performance and insights.

What We're Looking For

• A strong software engineering background with expertise in Python, C++, or comparable programming languages.
• 3+ years of experience in software engineering, machine learning, AI research, evaluation, or related technical disciplines.
• Experience designing, reviewing, or validating technical assessments, benchmarks, coding tasks, or evaluation methodologies.
• Familiarity with large language models, coding agents, reinforcement learning, model evaluation, or related AI systems.
• Proven ability to build tooling, automate workflows, and improve technical processes through systematic experimentation.
• Strong analytical skills with the ability to investigate model behavior and derive insights from complex technical systems.
• Excellent written and verbal communication skills, including the ability to clearly articulate technical findings to diverse audiences.
• Comfortable operating in fast-moving research environments with significant ambiguity and evolving priorities.

Preferred

• Experience working on frontier AI systems, coding agents, or model evaluation research.
• Deep interest in understanding how data, evaluations, and feedback mechanisms influence model capabilities.
• Track record of independently driving ambiguous technical or research projects from conception to execution.
• Experience designing benchmarks or datasets for machine learning systems at scale.
• Familiarity with agentic workflows, tool use, reinforcement learning, or post-training methodologies.
• Publications, open-source contributions, or demonstrated technical leadership in AI, machine learning, or software engineering.