Staff Software Engineer, AI Product Engineering

Thomson Reuters•Brooklyn, NY

4d•Hybrid

About The Position

Thomson Reuters is looking for a Staff Software Engineer, AI Product Engineering to help shape and build the next generation of AI-powered software for tax and accounting professionals. You will join the CoCounsel for Tax & Accounting organization, working in a fast-moving, product-focused environment that combines startup-style ownership with the scale and resources of Thomson Reuters. This is a highly hands-on Staff-level engineering role for someone who thrives in greenfield and 0→1 product development. You will be expected to take ambiguous product and technical problems, define the right architecture, build critical parts of the solution yourself, and help guide other engineers from prototype through production. This is a hybrid role, with opportunities based in New York City, Minneapolis–St. Paul, or Toronto. Candidates should be comfortable working onsite as part of the team’s hybrid schedule. Final interviews may also be conducted onsite at the hiring hub associated with the candidate’s location.

Requirements

Significant software engineering experience building and operating large-scale, production-grade backend systems.
Strong, recent hands-on development experience with Python.
Production experience with backend frameworks such as FastAPI, Flask, or Django, with FastAPI strongly preferred.
Experience designing APIs, backend services, data models, asynchronous systems, and distributed architectures.
Experience with relational databases such as PostgreSQL and building production systems on a major cloud platform; AWS experience is strongly preferred.
Demonstrated experience personally designing, building, and launching greenfield or 0→1 products and systems, with clear ownership from architecture through production.
Experience making architecture decisions that affect multiple systems, teams, or major product capabilities.
Strong system-design skills with the ability to reason through scalability, reliability, performance, failure modes, and operational trade-offs.
Experience taking software through the full lifecycle, including architecture, implementation, deployment, observability, debugging, incident response, and production iteration.
Demonstrated ability to influence technical direction without relying on formal authority.
Strong communication skills and experience partnering effectively across Engineering, Product, Design, AI/ML, and other technical stakeholders.
Ability to operate effectively in ambiguous, fast-moving environments and turn incomplete requirements into clear technical direction.

Nice To Haves

Hands-on production experience building generative AI or AI-native applications is strongly preferred.
Experience with LLM-powered agents, agent orchestration, tool/function calling, RAG, embeddings, vector search, retrieval systems, or real-time AI interactions is preferred.
Experience integrating commercial or open foundation models, including OpenAI, Anthropic, or similar providers, is preferred.
Experience building customer-facing SaaS or product software in a startup, scale-up, or similarly high-ownership environment is preferred.
Experience with Docker, Kubernetes, CI/CD, infrastructure as code, observability, and modern cloud-native engineering is preferred.
Exposure to TypeScript or modern frontend frameworks is helpful but not required.

Responsibilities

Own greenfield and 0→1 AI systems, taking ambiguous product problems from technical discovery and architecture through implementation, production launch, monitoring, and iteration.
Design and build production backend services using Python, FastAPI, PostgreSQL, and AWS.
Build and evolve AI-native applications incorporating large language models, AI agents, orchestration, retrieval, tool/function calling, and real-time AI workflows.
Integrate models and services from providers such as OpenAI, Anthropic, and other foundation-model platforms.
Design scalable APIs, data models, asynchronous workflows, and distributed services for high-volume, production-grade AI applications.
Make and influence architectural decisions across reliability, scalability, latency, observability, security, testing, failure handling, and maintainability.
Establish reusable engineering patterns and technical standards that improve consistency and velocity across multiple teams or workstreams.
Evaluate AI behavior in production and build mechanisms for quality measurement, experimentation, evaluation, guardrails, and continuous improvement.
Partner closely with Product and Design to translate complex customer problems into simple, scalable technical solutions.
Provide technical leadership across complex, cross-functional initiatives while remaining hands-on in design, implementation, debugging, and code review.
Mentor engineers, raise the technical bar, and help teams make sound architectural and engineering trade-offs.
Identify technical risks early and drive solutions across multiple systems or teams.

Benefits

Flexible vacation
Two company-wide Mental Health Days off
Access to the Headspace app
Retirement savings
Tuition reimbursement
Employee incentive programs
Resources for mental, physical, and financial wellbeing
Market competitive health, dental, vision, disability, and life insurance programs
Competitive 401k plan with company match
Competitive vacation, sick and safe paid time off
Paid holidays (including two company mental health days off)
Parental leave
Sabbatical leave
Optional hospital, accident and sickness insurance paid 100% by the employee
Optional life and AD&D insurance paid 100% by the employee
Flexible Spending and Health Savings Accounts
Fitness reimbursement
Access to Employee Assistance Program
Group Legal Identity Theft Protection benefit paid 100% by employee
Access to 529 Plan
Commuter benefits
Adoption & Surrogacy Assistance
Tuition Reimbursement
Access to Employee Stock Purchase Plan