Hire June Kim
I build research systems that can be inspected, rerun, and judged by their artifacts: benchmark audits, agent pipelines, evaluation harnesses, review protocols, and tools that turn AI research questions into working software.
I am an AI systems / research engineer. The depth is independent evaluation: I audit frontier coding benchmarks for construct validity and find where the headline metric measures the wrong thing, every verdict a re-runnable receipt. The breadth is shipping: 10+ years across Google, Loom, and startups, and fixes landed in dozens of open-source projects across Rust, Go, C++, and Python, which is what lets me read any benchmark's test suite in any stack.
The common thread is anti-credence, pro-merit: replace trust in credentials, affiliation, and polished claims with artifacts that earn trust through evidence, provenance, review, execution, and use.
The best fit is a team that needs someone between research and product: build the system, run it against real workflows, measure what breaks, and turn the result into a better product or protocol.
Forward me for
- Research engineering for AI systems, agents, coding tools, or evaluations.
- Applied AI work where ambiguous research ideas need to become shipped systems.
- Developer tools, AI infrastructure, or agentic workflow products.
- Founding or early engineering roles around research-adjacent products.
Proof
- Benchmark and eval auditing: five public construct-validity audits of frontier coding benchmarks (ProgramBench, SWE-bench Pro, DeepSWE, SWE-rebench, SWE-bench Verified), plus determinacy, a reusable auditing tool. Each verdict is a grep-checkable receipt anyone can re-run.
- The Hypothesis Graph: a harness-layer data structure for coding agents — testable-claim nodes, refutation-condition edges — that makes an agent's reasoning inspectable and reusable. arXiv-shape preprint with reproducible, DOI-archived artifacts.
- Verifiable Knowledge: a protocol where each agent presents each claim with a falsifiable condition another agent can re-run instead of attesting its own work. Accountable failure outranks unaccountable assertion.
- Slop Slope: tested whether adversarial review loops turn test-passing LLM code from coin-flip drafts into merge-ready artifacts.
- Agentic open-source contribution: pipeline for finding issues, generating patches, submitting PRs, and tracking maintainer outcomes.
- Cognition and epistemology research: abduction, memory, provenance, review loops, and agentic systems as a theory layer for visible tools.
- Sweep & Triage
- (PR) → merged
- PageLeft
- Runnable Textbooks
Resume & Contact
Resume page · PDF · Markdown