Thomson Reuters
Staff Software Engineer, AI Product Engineering
$137K–$293K
United States of America, Brooklyn, New York +4 · Hybrid · Staff · Full-time · Posted Oct 21, 2026
- Location
- United States of America, Brooklyn, New York
United States of America, Frisco, Texas
Canada, Toronto, Ontario
United States of America, Ann Arbor, Michigan
United States of America, Eagan, Minnesota - Experience
- Significant software engineering experience; no specific number of years stated.
About the job
Build and lead production AI-powered backend products for tax and accounting professionals, owning systems from architecture and implementation through launch and ongoing improvement.
What you'll do
- Own greenfield AI systems from discovery and architecture through production launch, monitoring, and iteration.
- Design and build scalable backend services, APIs, data models, and AI-native workflows.
- Set technical direction, establish reusable engineering standards, and mentor engineers across teams.
What they ask for
- Significant experience building and operating large-scale, production-grade backend systems.
- Strong, recent hands-on Python development and production experience with a backend framework such as FastAPI, Flask, or Django.
- Experience with APIs, data models, asynchronous and distributed systems, relational databases, and a major cloud platform.
- Demonstrated ownership of greenfield products and systems, including architecture, production deployment, and cross-team technical leadership.
Nice to have
- Hands-on production experience building generative AI or AI-native applications, including agents, orchestration, RAG, or retrieval systems.
- Experience with OpenAI or Anthropic, AWS, Docker, Kubernetes, CI/CD, infrastructure as code, or TypeScript.
Pay
$158,000 USD - $293,000 USD (base salary); $137,100 USD - $254,700 USD (base salary)
Tools
Python, FastAPI, Flask, Django, PostgreSQL, AWS, OpenAI, Anthropic
AI focus
Build AI-native applications using large language models, agents, orchestration, retrieval, tool/function calling, and real-time workflows; integrate models and evaluate AI behavior with quality measurement, experimentation, and guardrails.
Model-written summary · the original posting is authoritative