Senior Software Engineer, Handshake

• 6+ years of professional software engineering experience building backend systems, data infrastructure, or distributed platforms
• Strong programming skills in Python, TypeScript, Java, or similar languages
• Experience designing and operating large-scale data pipelines, distributed systems, or workflow orchestration platforms
• Strong system design skills, with experience making architectural decisions around scalability, reliability, observability, and maintainability
• Experience building cloud-native systems using AWS, GCP, or similar cloud platforms
• Familiarity with containerized execution environments (Docker, Kubernetes) and distributed job processing
• Strong understanding of relational and/or NoSQL databases, data modeling, and storage systems
• Ability to navigate ambiguity and translate evolving research or product requirements into scalable engineering solutions
• Excellent communication skills and a track record of partnering effectively with researchers, product managers, and cross-functional engineering teams
• Experience mentoring engineers and leading technical projects from design through production

Desirable

• As a Senior Software Engineer on our Coding Pod, you'll lead the design and development of the data infrastructure that powers frontier AI coding models
• This team sits at the intersection of applied machine learning, distributed systems, and developer tooling—building the large-scale benchmark datasets, evaluation frameworks, and execution environments that determine how state-of-the-art coding models are trained and measured
• You'll own critical platform investments end-to-end, from architecting scalable data pipelines and evaluation systems to establishing technical standards for dataset quality, reliability, and developer workflows
• Working closely with ML researchers, product managers, and engineers, you'll translate ambiguous research goals into production-ready systems that accelerate model development and improve evaluation quality
• This is an opportunity to shape the infrastructure behind next-generation AI coding systems while solving challenging distributed systems, data engineering, and developer platform problems at scale
• Architect and build scalable data infrastructure that powers the generation, transformation, validation, and delivery of large-scale coding datasets
• Design end-to-end evaluation systems, including automated grading, benchmarking, human-in-the-loop review, and quality assurance workflows
• Lead the technical design of developer-facing tooling and integrations with engineering ecosystems (GitHub, CI/CD systems, coding agents, containerized execution environments)
• Build reliable backend services and APIs that support dataset generation, evaluation pipelines, and experiment infrastructure
• Drive architectural decisions around distributed systems, workflow orchestration, execution environments, and data quality
• Partner closely with ML researchers to translate evolving evaluation methodologies into scalable engineering systems
• Improve platform reliability, observability, and performance through monitoring, debugging, and operational excellence
• Mentor engineers through technical design reviews, code reviews, and architectural guidance while helping raise the engineering bar across the team
• Identify opportunities to standardize reusable infrastructure, tooling, and evaluation frameworks that accelerate future model development