The law firm for companies that need senior counsel without a traditional firm. Backed by Y Combinator.
About the role
The legal industry is broken — lawyers bill by the hour, charge enormous sums, and take forever to respond. This means that a lot of people who need legal advice don't get it. We're using AI to solve this problem. AI takes the first pass at everything our lawyers do, so that the lawyers can focus on human interaction. We're starting with negotiating commercial contracts, and will expand quickly from there to other use cases within legal. We're reimagining what a contract can be in the AI age.
What they're looking for
- 5+ years of software engineering, machine learning engineering, or infrastructure experience
- Strong software engineering fundamentals and proficiency in Python
- Experience building production infrastructure for machine learning systems
- Experience with cloud infrastructure, distributed systems, and containerized workloads
- Ability to independently design, build, and operate complex technical systems
- Computer Science degree or equivalent experience
More about this role
The legal industry is broken — lawyers bill by the hour, charge enormous sums, and take forever to respond. This means that a lot of people who need legal advice don't get it. We're using AI to solve this problem. AI takes the first pass at everything our lawyers do, so that the lawyers can focus on human interaction. We're starting with negotiating commercial contracts, and will expand quickly from there to other use cases within legal. We're reimagining what a contract can be in the AI age. We're a veteran team, with one successful exit already, who've been doing deep learning in the legal space since long before ChatGPT.
General Legal is seeking a Senior ML Infra Engineer to build the systems that allow us to rapidly experiment with, evaluate, train, and deploy increasingly capable AI systems.
You'll sit at the intersection of research and production engineering. Your job will be to make our researchers and engineers dramatically more effective: building reliable infrastructure for model experimentation, evaluation, inference, data generation, training, and observability while ensuring that promising ideas can move quickly from an experiment into production.
This is a unique...
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