Research Engineer
Ramp Business · New York, NY · San Francisco, CA
Own Ramp Labs' benchmarking effort, turning real finance workflows into benchmarks and RL environments for AI agents. Build graders, run evaluations across models and agent versions, and feed results into post-training decisions.
What you'll do
- Build versioned benchmark suites from agent traces, customer edge cases and expert labels
- Decide what to measure and report benchmark results across models and agents
- Design graders using rubrics, programmatic checks and LLM-as-judge
- Turn evals into reward functions and RL environments, catching reward hacking and contamination
- Publish external benchmarks and write-ups on model performance on finance work
Requirements
- Hands-on experience building LLM benchmarks or evals used for real decisions
- Strong Python and software engineering; reproducible, fast eval code
- Statistical judgment: variance, confidence intervals, sample size, inter-rater agreement
- Working knowledge of post-training (SFT, RL with verifiable rewards)
- Comfort with SQL and large datasets, working from production traces
- Taste for what is worth measuring
Nice to have
- 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)
- Familiarity with accounting, finance or other expert-domain work
- Public work such as a benchmark, eval suite or failure-analysis write-up
Benefits
- Flexible PTO
- Home-office equipment ordering
- Health and wellness stipend
- Budget for intra-office travel
- Weekly coffee stipend
- 100% medical, dental & vision coverage for employee, partial for dependents
- One Medical annual membership
- 401(k) with employer match
- Fertility HRA (up to $10,000 per year)
- Parental leave up to 16 weeks at 100% pay
- Pet insurance
- In-office lunch, snacks and drinks
- Relocation expense coverage to NYC or SF
Skills
Outlined skills are listed as required.
Full original description
About Ramp
Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books.
The problems are high-stakes, data-dense, and unforgiving.
We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome.
The median Ramp customer saves 7.5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same.
If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it.
About Ramp Labs
Ramp Labs is Ramp's AI research group. We post-train models on finance work, build the environments and evals they learn from, and ship them into the products 70,000+ companies use to move money. We think the defensible part of an AI product is the loop: proprietary environments, evals that track what customers care about, and models trained against both.
About the Role
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.
What You’ll Do
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
What You Need
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
Nice-to-Have
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
Benefits available to all full-time Ramp employees (Global)
Flexible PTO
Centralized home-office equipment ordering
Health and wellness stipend
Budget for intra-office travel
Weekly coffee stipend
United States
100% medical, dental & vision insurance coverage for you, with partial coverage for dependents
One Medical annual membership
401(k), including employer match on contributions made while employed by Ramp
Fertility HRA (up to $10,000 per year)
Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay
Pet insurance
In-office perks: lunch, snacks, drinks, and more
Relocation expense coverage to NYC or SF (if needed)
Canada
Group medical, dental, and vision coverage through Sun Life
Life, AD&D, and disability coverage
Fertility drug coverage (up to $4,000 lifetime)
Group Retirement Plan with employer match (RRSP + DPSP)
Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay
Employee Assistance Program and virtual care through Lumino Health
United Kingdom
Private medical insurance through Freedom Elite
Virtual GP and at-home care via eMed x Livi
Workplace pension through Penfold, with salary sacrifice option
Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay
Referral Instructions
If you are being referred for the role, please contact that person to apply on your behalf.
Other notices
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Beware of recruiting scams: Ramp will only contact you through official @Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.