Software Engineer, Handshake

• This role is ideal for engineers who enjoy working across backend systems, data pipelines, and developer tooling, and want direct impact on the future of AI-powered software development
• 2+ years of professional software engineering experience, with exposure to backend systems or data engineering
• Strong programming skills (e.g., Python, TypeScript, or similar)
• Experience building or maintaining data pipelines, ETL systems, or distributed systems
• Familiarity with containerization (e.g., Docker) and cloud infrastructure (AWS, GCP, or similar)
• Understanding of databases and data modeling (SQL or NoSQL)
• Comfort working across systems boundaries, from infrastructure to developer-facing tools
• Strong problem-solving skills and attention to detail, especially in data quality and correctness
• Effective communication skills and ability to collaborate cross-functionally

Desirable

• As a Software Engineer on our Coding Pod, you will build the data infrastructure and pipelines that power frontier AI coding models. This team sits at the intersection of applied machine learning, data engineering, and product development—creating large-scale, high-quality benchmark datasets that evaluate how models perform on real-world, economically valuable coding tasks
• You’ll own end-to-end data pipelines—from task generation and dataset construction to quality assurance and delivery—helping define how next-generation coding models are trained and evaluated. You’ll also work with human-in-the-loop evaluation systems, agentic coding tools, and automated assessment frameworks to ensure dataset quality and model performance
• Design and build scalable data pipelines for generating, transforming, and validating large-scale coding datasets
• Develop systems for task generation, dataset curation, and quality assurance, including automated and human-in-the-loop evaluation workflows
• Integrate with developer ecosystems (e.g., GitHub) and build tooling that supports real-world coding environments
• Work with containerized environments (e.g., Docker) to safely execute and evaluate code at scale
• Build backend systems and APIs that power dataset delivery and model evaluation pipelines
• Collaborate closely with ML researchers, product managers, and other engineers to define evaluation methodologies and improve dataset quality
• Implement automated grading, benchmarking, and assessment systems for coding tasks
• Debug and optimize pipeline performance, reliability, and scalability across distributed systems
• Contribute to architectural decisions around data infrastructure, evaluation systems, and pipeline orchestration