wow_its_tru

Tome | SF (US) | Onsite or Remote | https://tome.com

Tome revolutionizes contract understanding using LLMs fine-tuned on contract domain knowledge. For users, we decode complex contracts into simple reports, so everyone can understand their rights and make informed decisions. We are a team of 8, with a 4.5 person engineering team, backed by top VC firms, and we're poised to transform the $400B legal industry.

Join us as a Senior Full Stack Engineer to build out our core product UI, our AI backend, and our RLHF internal tooling. We have tons of legal problems (the good kind), tons of AI problems (also the good kind), and most interesting, the intersection between the two. We're working on graph visualizations of legal information - come help!

Ideal candidate: has worked in a startup, or independently led projects; 5+ years of frontend development experience; Experience with complex data visualizations; and bonus points if you like writing full end-to-end tests, improving CI pipelines, or implementing pretty color gradients.

Tech Stack:

- Next.js (all-in on the Vercel ecosystem), Python, Postgres, AWS

Benefits: early-stage startup growth opportunities; competitive pay and comprehensive benefits; use of a collection of 10,000,000 board games; home office stipend; use of the company jet* (*when acquired)

We are committed to diversity and encourage applications from underrepresented groups.

Reach out to me: st@tome.com and mention HN!

Tome | SF (US) | Onsite or Remote | https://tome.com

Tome revolutionizes contract understanding using AI coupled with expert domain knowledge. We decode complex contracts into simple reports, so users can understand their rights and make informed decisions. We are a team of 8, with a 4.5 person engineering team, backed by top VC firms, and we're poised to transform the $400B legal industry.

*Role*: Join us as a Senior Frontend/Full Stack Engineer to build out our customer facing UI as well as our suite of internal tools for labeling contracts, writing prompts, viewing prompt performance, working with our custom expression language, and the usual et cetera. Lots of room for experimenting on best approaches to graph based interfaces and complex data visualizations.

*Ideal Candidate:*

- Has worked in a startup, or independently led projects - 5+ years of frontend development experience - Experience with complex data visualizations - Bonus points if you like writing full end-to-end tests, improving CI pipelines, or implementing pretty color gradients.

*Tech Stack:*

- Python backend, postgres databases, AWS infra, Next.js frontend

*Benefits:*

- Early-stage startup growth opportunities - Competitive pay and comprehensive benefits - Periodic on-sites (often including board gaming [presuming you view that as a benefit]) - Home office stipend - Use of the company jet* (*when acquired)

We are committed to diversity and encourage applications from underrepresented groups.

Reach out to me: st@tome.com and mention HN!

We're building exactly this today, for common business contracts.

We're not building for consumers today, because I think it's vanishingly unlikely that you'll, like, pick a different car rental company once you read their contract :) but leases, employment contracts, options agreements, SaaS agreements... all common, all boilerplate with 5-10 areas to focus on, all ready for LLMs!

We're building exactly this for contract analysis: upload a contract, review the common "levers to pull", make sure there's nothing unique/exceptional, and escalate to a real lawyer if you have complex questions you don't trust with an LLM.

In our research, we found out that most everyone has the same questions: (1) "what does my contract say?", (2) "is that standard?", and (3) "is there anything I can/should negotiate here?"

Most people don't want an intense, detailed negotiation over a lease, or a SaaS agreement, or an employment contract... they just want a normal contract that says normal things, and maybe it would be nice if 1 or 2 of the common levers were pulled in their direction.

Between the structure of the document and the overlap in language between iterations of the same document (i.e. literal copy/pasting for 99% of the document), contracts are almost an ideal use-case for LLMs! (The exception is directionality - LLMs are great at learning correlations like "company, employee, paid biweekly," but bad at discerning that it's super weird if the _employee_ is paying the _company_)