Staff Software Engineer – AI Platform

Thomson Reuters is building the AI platform that will power the next decade of tax, accounting and audit products, including CoCounsel Audit, a suite of AI‑native products used by accountants on real client work every day. As a Lead Software Engineer (Staff Engineer), you will own core backend and AI orchestration systems that turn frontier models into reliable, production‑grade workflows at scale. This role is for the CoCounsel for Audit team, formed from the recent acquisition of the startup Materia.

About the role:

You will be a Lead Software Engineer (Staff Engineer) responsible for the backend and orchestration layer powering AI agents, conversations, workspaces, and knowledge base across CoCounsel for Audit.

• Shape the team's AI platform and patterns: Define patterns for building MCP servers, designing agents, novel uses of LLMs, experimentation, and scalable infrastructure. Your decisions will influence your product area and adjacent teams, not just a single feature.
• Shape the platform integration strategy across new and existing Thomson Reuters audit systems to provide world-class content and solutions for our customers.
• Work at real production scale: Build and evolve systems that operate over millions of documents, highly structured tax data, ever-changing laws, and thousands of concurrent AI interactions from accountants doing time‑sensitive work.
• Small team, big surface area: Join a tight group of senior engineers and researchers shipping quickly, with direct access to product leadership and customers. Think ownership of a startup with the runway of a public company.
• Great fit for people who enjoy and thrive at setting technical direction, mentoring senior engineers, and spending most of their time in the code and architecture of AI systems.

What you'll do:

Technical leadership and cross‑functional influence

• Lead multi‑quarter initiatives that cut across AI, product, and infra (e.g., a new orchestration layer, a low-latency retrieval system, or a unified knowledge base).
• Mentor senior and mid-level engineers, raising the bar on system design, code quality, and AI integration practices across the org.
• Be a go-to expert in new model capabilities, collaborating closely with AI/ML engineers, researchers, designers, and PMs to translate industry improvements into reliable, user‑facing workflows that accountants trust.
• Help shape the team’s roadmap, technical strategy, and engineering culture – from experimentation practices to testing, rollout, and postmortems.

Design and own AI‑first backend systems

• Provide your technical POV on architecting and implementing backend services (Python, FastAPI, PostgreSQL, AWS, Vercel) that power generative AI agents, complex workflows (e.g., tax filing, advisory, audit), and document‑centric experiences.
• Build and evolve AI orchestration: routing, tool calling, MCP servers, multi‑step workflows, safety and guardrails, and robust error handling around third‑party LLMs (OpenAI, Anthropic, and others).

Scale, reliability, and performance

• Design for high‑throughput, low‑latency AI workloads: caching, queuing, rate‑limiting, model failover, and cost/performance trade-offs.
• Work with large‑scale data: millions of documents, retrieval and search, vector stores, and indexing strategies tailored to tax and accounting use cases.
• Establish and refine SLOs, observability, and incident response for AI systems that must be correct, auditable, and trustworthy in professional workflows.

About You:

You are a fit for the position of Lead Software Engineer (Staff Engineer) if your background includes:

Required Experience & Skills:

• Bachelor’s degree in computer science, Computer Engineering, a related field, or equivalent experience
• 5-7+ years of experience in full-stack development, building scalable cloud-based applications, web services, APIs and AI-driven products.
• Deep Python expertise and experience with production systems using frameworks like FastAPI (or similar), relational databases (PostgreSQL or equivalent), and a major cloud provider (AWS preferred).
• Strong background in distributed systems: data modeling, API contracts, observability, resilience patterns, and performance tuning under load.
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