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Draftwise is an AI-powered contract drafting, review, and negotiation platform used by top law firms and legal departments globally, including over half the Vault 10 and dozens of Am Law 100 firms. The company is Y Combinator-backed, raised a $20M Series A from Index Ventures, and is headquartered in NYC with offices in London and West Palm Beach.
As a Forward Deployed AI Engineer (Backend), you will embed with client law firms to understand how their lawyers work and extend the product to match their workflows. You'll model their precedent and playbooks into the company's legal ontology and build the backend systems that power it. This role is weighted toward backend engineering and AI systems, offering early-career engineers the opportunity to learn forward deployment practices from engineers who built these practices at Palantir.
Key responsibilities include:
- Ontology modeling for legal knowledge: Transform each firm's precedent, playbooks, and preferred language into structured data the product can reason over. This is the most transferable skill in the role.
- Backend services: Own features end-to-end across Postgres, OpenSearch, graph databases, and AWS services. You'll learn how these pieces fit together—a rare early-career opportunity.
- LLM systems in production: Write and test prompts, build retrieval and evaluation scaffolding, and measure quality on real customer data. This is infrastructure work, not prompt tuning by feel.
- Scale and performance: Handle large documents and precedent sets under interactive latency. Profiling, query tuning, and cost optimization are regular engineering tasks.
- Customer deployments: Own pieces of firm rollouts alongside senior engineers, working out why playbooks don't map cleanly to the product.
- Frontend as needed: Ship UI changes in TypeScript and React without waiting on others.
You should bring high ownership and low ego, a focus on user impact, and curiosity over credentials. Backend should be your center of gravity—you're comfortable with SQL, API design, and debugging unfamiliar services. You'll be in the customer room asking sharp questions and translating vague legal problems into models, schemas, and code. Interest in LLMs and their limits is important; judgment about where they help and where they cost time matters more than production LLM experience.
You don't need legal industry experience, prior customer-facing work, or a specific language background. You do need to have built real backend software and the curiosity to learn legal concepts, ontologies, and unfamiliar codebases.
