Senior Software Engineer, Coding

Handshake Handshake · Enterprise · San Francisco, CA · Engineering

Senior Software Engineer on the Coding Pod to lead the design and development of data infrastructure for frontier AI coding models. This role involves building large-scale benchmark datasets, evaluation frameworks, and execution environments, and owning critical platform investments end-to-end. The engineer will translate research goals into production-ready systems, focusing on distributed systems, data engineering, and developer platform problems at scale.

What you'd actually do

  1. Architect and build scalable data infrastructure that powers the generation, transformation, validation, and delivery of large-scale coding datasets.
  2. Design end-to-end evaluation systems, including automated grading, benchmarking, human-in-the-loop review, and quality assurance workflows.
  3. Lead the technical design of developer-facing tooling and integrations with engineering ecosystems (GitHub, CI/CD systems, coding agents, containerized execution environments).
  4. Build reliable backend services and APIs that support dataset generation, evaluation pipelines, and experiment infrastructure.
  5. Drive architectural decisions around distributed systems, workflow orchestration, execution environments, and data quality.

Skills

Required

  • 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.

What the JD emphasized

  • building the large-scale benchmark datasets, evaluation frameworks, and execution environments that determine how state-of-the-art coding models are trained and measured
  • architecting scalable data pipelines and evaluation systems
  • establish technical standards for dataset quality, reliability, and developer workflows
  • translate ambiguous research goals into production-ready systems
  • solving challenging distributed systems, data engineering, and developer platform problems at scale

Other signals

  • building the fastest-growing AI data business
  • supports all of the frontier AI labs
  • architecting scalable data pipelines and evaluation systems
  • accelerate model development and improve evaluation quality