Software Engineer- Benchmarking

Prepare and maintain benchmark datasets: Own the data work behind our benchmarks, including cleaning, preparation, conversion into runnable formats, and ongoing maintenance. Validate that tasks are complete, consistent, and executable, and flag ambiguities that would compromise results.

Build and maintain evaluation pipelines: Build the infrastructure that runs evaluations consistently across model APIs and terminal agents, so results are reproducible and comparable.

Build evaluation environments: Create lightweight, containerized environments and viewers for tasking and for evaluating model performance on tool use.

Support model experiments: Help fine-tune small open-source LLMs and compare baseline against post-training performance.

Develop the scoreboard and leaderboard: Build the published views of our results, including model-level, benchmark-level, task-level, domain-level, and rubric-level performance.

Build analysis tools: Make it easy to identify recurring failure modes, compare models and agent scaffolds fairly, and track capability improvements and regressions over time.

Collaborate: Work closely with researchers and engineers to make sure evaluation data and outputs are accurate, consistent, and well integrated into what we publish.

Build tooling for data creation and review: Support expert annotation and data-generation projects by building lightweight HTML viewers and internal tools for task authoring, review, quality control, and structured data collection.

Solid engineering skills: 4+ years of professional experience building and maintaining complex systems, with strong Python. You write robust, maintainable code and are comfortable diving deep into existing codebases and infrastructure.

Data rigor: Experience preparing, cleaning, and maintaining datasets, and the care to make sure two results are genuinely comparable.

Comfort with containers and environments: Experience with Docker and building reproducible execution environments.

Collaborative: You work well alongside researchers and scientists and can translate their methodology into working systems.

Hands-on experience running AI evaluations, or with frameworks like Harbor, Terminal-Bench, or Inspect, is a strong plus.

Evaluations: Python, model APIs, agent/evaluation frameworks, custom evaluation tooling

Models: APIs from the major AI providers, terminal agents, and open-source models via the Hugging Face ecosystem and PyTorch

Environments: Docker

Publishing: React, Next.js, Tailwind

Workflow: GitHub, Slack, Notion, Linear

Experience fine-tuning or post-training open-source LLMs, or other hands-on machine learning work

Experience with agentic, multi-turn, long-context, or tool-use evaluation

Experience validating LLM-as-judge or rubric-based grading setups

Background or strong interest in a scientific or technical domain

Experience building data-heavy dashboards, leaderboards, or visualizations

Open-source contributions or published work related to benchmarks and measurement

Full-time offers include base salary, equity, and benefits.