Senior Software Engineer, Coding

About Handshake

Handshake was founded on a simple belief that everyone deserves a path to a great career, regardless of where they went to school or who they know. Today, we power 25 million job seekers, 1 million+ employers, and 1,600 educational institutions. In 2025, we started Handshake AI and built the fastest-growing AI data business in history. We work directly with frontier AI lab researchers to create evaluations, publish benchmarks, and push the boundary of data. We’ve grown from $0 to ~$1B run rate and pay ~$60M to over 30K individuals every month.

About Handshake AI

Human data is the core infrastructure to AI advancement. Frontier AI labs currently improve model capabilities with various data-intensive post-training techniques. We believe that data spend for AI training will increase by 3-5x in the next few years and continue for much longer as models take on new domains. Handshake AI supports all of the frontier AI labs, working on their most complex data at the largest scale.

The Role

• 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.

What You'll Do

• 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.

What We're Looking For

• 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.

Nice to Have

• Experience building ML data infrastructure, evaluation frameworks, benchmarking systems, or dataset generation pipelines.
• Experience with coding agents, AI-assisted software development tools, or developer productivity platforms.
• Familiarity with GitHub APIs, developer ecosystems, CI/CD platforms, or code execution environments.
• Experience with workflow orchestration frameworks such as Airflow, Temporal, Dagster, or similar distributed job systems.
• Experience building automated testing, grading, or code execution platforms.
• Background working on infrastructure, platform engineering, or developer tooling in high-growth environments.
• Familiarity with LLM evaluation, coding benchmarks, or agentic software engineering systems.
• Passion for advancing AI-powered software development through scalable engineering infrastructure.

Compensation Range

$225K - $250K