Senior Generative AI Engineer

Cleary Gottlieb Steen & Hamilton LLP•New York, NY

18h•Remote

About The Position

We are an internal team at Cleary Gottlieb building bespoke AI solutions for legal work, drawing on software engineering, data science, and deep domain expertise. We work at the boundary of enterprise-grade software, and we are regularly building services and applications nobody here has built before. That makes for a dynamic learning environment — and one where a junior engineer will gain exposure to real-world AI systems from day one. We are a tight-knit, remote-first team that values growth, honesty, and curiosity. We work collaboratively across multiple disciplines — engineering, data science, legal domain experts, and product — to deliver meaningful impact. Junior engineers are paired with experienced mentors and participate in structured code reviews, pair-programming sessions, and weekly knowledge-sharing stand-ups. You will join as a Senior Generative AI Engineer on the AI Acceleration Team, reporting to the Data Science Manager, and working day to day with our engineers, data scientists, and legal domain experts. Much of the infrastructure and tooling described below is already under way; what we need is someone to own and advance the AI systems that sit on top of it. Nobody arrives knowing legal workflows or our stack, and you will have the Data Science Manager and the rest of the team alongside you while you pick them up. Judgement, curiosity, and an appetite for more responsibility as we grow count for more here than the length of a CV.

Requirements

At least 3 years of professional experience building and deploying AI systems, with at least 1-2 years focused on LLM/GenAI applications in production (not just prototypes or research)
Deep experience with document-heavy NLP: extraction from complex layouts (PDFs, tables, scanned documents), entity recognition, and structured output generation
Proven ability to design and optimise RAG pipelines and prompt architectures for accuracy, cost, and latency in production
Strong Python engineering skills.
Familiarity with at least one LLM orchestration framework (LangGraph, LlamaIndex, or equivalent) and at least one vector database (Pinecone, Weaviate, pgvector, or equivalent)
Experience with cloud platforms (AWS or Azure preferred) for deploying and monitoring LLM-backed services, including CI/CD, containerisation, and observability tooling
Experience designing systematic evaluation methodologies for generative AI (automated evals, golden test sets, faithfulness/hallucination metrics)
Ability to own a workstream end-to-end: scope it, build it, evaluate it, ship it, and clearly communicate tradeoffs and results to non-technical stakeholders
A genuine interest in legal work is essential.
This is a hands-on role focused on building practical solutions that lawyers will use daily, not academic research.

Nice To Haves

Experience in a startup or high-velocity AI team where you shipped frequently and wore multiple hats
Master's or PhD in Computer Science, Computational Linguistics, Mathematics, or a related quantitative field
Vector databases, retrieval systems, or knowledge graphs experience
Familiarity with model-serving infrastructure (vLLM, TGI, Triton) and GPU-aware deployment
Domain experience in legal tech, compliance tech, or other regulated industries where accuracy and auditability are non-negotiable
Published research or significant open-source contributions in NLP, information retrieval, or generative AI
Experience with knowledge graphs, ontologies, or semantic reasoning over structured legal data
Experience with Spark or Databricks and related technologies
Research fluency (reading papers, reproducing techniques) is a plus, not a substitute.
A legal background is not required
Experience with document automation or familiarity with legal workflows will be considered a significant advantage.

Responsibilities

Own production AI systems end-to-end: Design, build, deploy, and monitor LLM-powered document analysis pipelines (extraction, classification, risk flagging) that serve lawyers daily, meeting defined latency SLAs and accuracy benchmarks
Data Engineering: Transform legal data into structured, high-quality datasets that power our AI systems.
Design and ship agent workflows: Architect multi-step, multi-agent systems for complex legal tasks (e.g. due diligence, contract review, regulatory analysis) with robust state management, tool calling, and human-in-the-loop checkpoints
Define evaluation and governance frameworks: Build golden test sets, automated eval pipelines, and regression suites. Implement guardrails (prompt firewalls, output filters, PII redaction) to ensure safety and regulatory readiness
Optimise cost and performance: Manage token budgets across model providers, implement caching and batching strategies, and make data-driven build-vs-buy decisions on model selection (API-served, open-source vs. proprietary)
Collaborate with lawyers and product: Translate ambiguous legal workflows into structured AI problems, define acceptance criteria with domain experts, and iterate based on user feedback and eval results