Senior Staff Software Engineer, Legal Automation

Coinbase's Legal organization is at an inflection point. We manage corporate governance for 60+ global subsidiaries, public company reporting, equity programs, a venture portfolio, M&A, every institutional client agreement, and product counsel for all of Coinbase's PGs — from tokenized equities to stablecoin launches. Our clients are the CEO, COO, and every product group at the company. We work at the speed of a crypto startup with the compliance requirements of a public financial institution.

We are not looking to incrementally improve. We are looking to reimagine how legal work gets done at scale — replacing manual, high-volume workflows with AI-native systems, building an agent platform where today's junior lawyers become tomorrow's agent builders, and proving a model for how every non-technical org at Coinbase can transform with AI.

We have a roadmap, we have playbooks, and we have deep domain knowledge. What we need now is a technical architect to build the platform that brings this vision to life.

What You'll Do:

• Architect and build Legal AI infrastructure — Design and ship a platform of AI agents and automated workflows that replace high-volume, templated legal work including institutional client agreement redlining, SEC filing support, subsidiary governance workflows, and commercial deal processing.
• Own the institutional client agreement automation system — Our highest-priority use case: build an end-to-end agentic workflow that ingests counterparty markups, evaluates them against a structured playbook, generates a first-pass redline with comments, routes for human review, and tracks outcomes. This is a production-grade system affecting hundreds of institutional clients in pipeline.
• Build a cohesive agent platform and governance layer — Consolidate a fragmented set of ad-hoc bots and agents into a consistent, documented, maintainable platform with naming conventions, personas, observability, and standards for how Legal agents are built and deployed.
• Act as the technical thought partner to the VP and the broader Legal leadership team — Translate complex legal workflows into engineered systems, and translate engineering constraints back to non-technical stakeholders. The ability to bridge these worlds is as important as the engineering itself.
• Define the make-vs-buy strategy — Evaluate and retire third-party legal tech tools in favor of purpose-built internal solutions where Coinbase's engineering strength gives us a structural advantage.
• Be the case study — This role is explicitly designed to be a pilot for forward-deployed engineering in non-technical organizations at Coinbase. Success here creates the playbook for how other functions (Finance, HR, Operations) can undergo the same transformation.

Required Skills and Experience:

• 12+ years of backend software engineering experience, with a track record of shipping production systems at scale
• Proven experience getting LLM-based applications to production — including evaluation frameworks, grounding, guardrails against hallucination, and human-in-the-loop design
• Multi-agent orchestration experience — you've designed and operated systems where multiple AI agents collaborate, hand off work, and escalate to humans appropriately
• Exceptional ability to communicate with and learn from non-technical domain experts — you don't need to know legal terminology on day one, but you need to rapidly internalize complex workflows and translate them into engineered systems
• Platform mindset — you think in primitives, reusability, and standards, not just point solutions
• Distributed systems fundamentals — the agreement automation system will touch document processing, structured playbook evaluation, routing logic, and audit trails; you need to architect this correctly from day one
• Relentless drive to set and achieve ambitious goals in ambiguous, greenfield environments
• Demonstrates the ability to responsibly use generative AI tools and copilots (e.g., LibreChat, Gemini, Glean) in daily workflows, continuously learn as tools evolve, and apply human-in-the-loop practices to deliver business-ready outputs

Job ID: P77646