Outbound Sales Engineer
•SerpApi· 3y
💻 Engineering⏰ Full-time📈 Mid Level💰 $80k-100k🌍 Worldwide
3y ago
Crosby·20 days ago
Crosby is built on a simple conviction: a great legal system is the watermark of a great society — and the best legal work comes from combining human expertise with the right technology, not replacing one with the other.
Crosby is the first AI-native law firm, helping ambitious companies like Cursor, Ramp, and Cognition sign commercial contracts faster.
Legal work is both art and science, and we're mapping the frontier between the two — codifying what can be systematized, amplifying human judgment where it matters most. That's why the right way to bring AI into law isn't to sell software and walk away. It's to own the outcome together: higher-quality work, delivered faster. We build proprietary tools and human-in-the-loop workflows that change what's possible for corporate legal teams.
Crosby was founded by Ryan (Stanford Law, Cooley, startup GC) and John (Penn, first 15 engineers at Ramp). We work together, in person, in New York City — because the best ideas still happen in the same room. Help us transform one of society's most important industries.
We’re launching the Crosby Intelligence Fellowship, a program to accelerate research on frontier problems in legal AI and automated negotiation. Two selected Fellows will split $50,000 in grants and $25,000 in Codex credits to pursue individual, focused research projects.
Contracts underpin every economic transaction. They are the rails of commerce. Yet negotiating them is still slow, expensive, and opaque.
AI has made its fastest progress in domains with verifiable answers: math, code, games like chess and Go. Legal negotiation is different. There is rarely a single right move — the information is incomplete, much of what matters goes unsaid, and the best play depends on who is across the table. It is closer to poker than to math: great attorneys read the other side, weigh what they will accept, and judge when to press, when to concede, and when to hold firm under uncertainty. They anticipate how a counterparty will respond, and adjust as the hand unfolds.
In order for legal AI to progress, the industry needs to get better at empiricism — measuring and defining quality rigorously while preserving the judgment that is central to great legal work. These challenges make legal AI the next research frontier, and the hardest problems won’t be solved by any one team. We want to engage the research community to solve them with us.
Crosby Intelligence is the research-focused arm of Crosby Legal, an AI-native law firm, which means we work every day on the problems (and with the domain experts) that this research needs.
Compensation. A $25,000 stipend for the fellowship.
Compute. $12,500 in Codex credits.
Expert access. Regular sessions with practicing attorneys for problem framing, annotation, and evaluation.
Data. Access to Crosby’s private (non-client) collection of sample contracts and histories
Publication. Fellows retain the right to publish their work.
By the end of the program, we aim for every Fellow to have produced a published paper, benchmark, or open-source artifact.
The problems outlined below are loose guidance. We encourage you to propose your own ideas if they are at the frontier of legal AI and align with your research.
1. Reward models under expert disagreement. In code, you can verify the answer. In legal work, two senior attorneys redlining the same clause will often disagree, and both versions can be defensible. That disagreement is lost when averaged away by standard preference modeling. How do you build reward models that learn from expert disagreement and separate taste from error?
2. High-fidelity negotiation simulation. Great redlines anticipate the counterparty's response, so great lawyers will rehearse against simulated counterparties. Out of the box, an LLM playing opposing counsel concedes too fast, fails to hold a position for strategic reasons, and doesn't trade concessions the way a real attorney does. How do you align agents that behave like real opposing counsel from scarce negotiation records? How do you evaluate whether the simulator predicts what real counterparties actually do? What does an RL environment look like for negotiations?
3. The Surgical Editing Benchmark. A senior attorney reshapes an entire deal by changing a single word, while an LLM haphazardly rewrites the entire paragraph. Coding agents show the same pathology, and will refactor a file to fix a one-line bug. These are the same problem: minimal-diff editing under a sufficiency constraint. A robust benchmark must jointly measure minimality and whether the edit achieves the objective
4. Knowing when to hand off to a human. The bar for verifying legal work is exceedingly high, and verification is expensive. An agent that can flag which parts of its own output need especially thorough review (instead of treating every line as equally trustworthy) would improve downstream quality, reliability, and efficiency. When should an AI legal agent escalate to a human, and how do you train that judgment from sparse expert-override data? More broadly: how do you give language models calibrated uncertainty?
5. Bring your own problem. If you're working on something else in legal AI, contract intelligence, or automated negotiation, we'd love to see it. Strong submissions make progress measurable, explain why the obvious approach fails, and will generalize beyond a single product.
Fellows may be PhD students, postdocs, faculty, or independent researchers. You may be a good fit if you have a strong background in machine learning or NLP, can execute a research project independently while incorporating feedback, and are excited about problems where ground truth is messy and the data is confidential. No legal background required. You must have valid work authorization in the country you'll be working from for the duration of the program.
Submit your resume/CV and a one-page research proposal: the question, why existing approaches fall short, your approach, and what you'll deliver.
Applications close: July 17, 2026
Fellows announced: July 31, 2026
Questions? Contact [email protected]
Crosby is an equal opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the New York City Fair Chance Act.
Pursuant to New York Labor Law Section 194-b, the US Pay Range for this position is listed in the job post. Final compensation will be determined based on skills, experience, and qualifications.