Ramp Research Engineer at Ramp Labs owning the benchmarking effort for AI models in finance workflows. Responsible for building benchmarks, designing graders, and closing the loop with post-training to improve model performance.
Responsibilities
Every model decision at Ramp Labs runs through a benchmark. Which model to route to. Whether a post-training run worked. Whether an agent is ready to post a corrective journal entry to a customer's ledger without a human in the loop.
You'll own Ramp's benchmarking effort. You'll turn real finance work (expense policy review, invoice coding, month-end close, spreadsheet Q&A) into benchmarks and RL environments, build the graders, and tell the team what is actually improving.
Build benchmarks from real workflows: turn agent traces, customer edge cases and expert labels into versioned benchmark suites for long-horizon agents that run for hours across tools and systems, plus tool use, multi-step reasoning and financial judgment
Own the benchmarking effort: decide what we measure, run benchmark suites across models and agent versions, and report results the team makes decisions on
Design graders: rubrics, programmatic checks and llm-as-judge, calibrated against human agreement, with a clear view of where each one breaks
Close the loop with post-training: turn evals into reward functions and RL environments, and catch reward hacking and contamination before they ship
Publish external benchmarks and write-ups on how models perform at real finance work
Qualification
Strong Python and software engineeringStatistical judgmentWorking knowledge of post-training (SFTComfort with SQL and large datasetsTaste for what is worth measuringIndustry experience on an evalFamiliarity with accountingPublic work we can look at
Required
Hands-on experience building LLM benchmarks or evals that people used to make real decisions
Strong Python and software engineering; your eval code is reproducible and fast
Statistical judgment: variance, confidence intervals, sample size, inter-rater agreement, and knowing when a delta is noise
Working knowledge of post-training (SFT, RL with verifiable rewards) and how evals feed into it
Comfort with SQL and large datasets; you'll work directly from production traces
Taste for what is worth measuring
Industry experience on an eval, benchmarking or post-training team
Built RL environments or verifiers for agentic tasks
Published benchmark or evaluation research (NeurIPS Datasets & Benchmarks, ICML, ACL)
Built or contributed to eval frameworks (verifiers, lm-evaluation-harness, HELM or similar)
Familiarity with accounting, finance or other expert-domain work
Public work we can look at: a benchmark, an eval suite, a failure-analysis write-up
