Staff Software Engineer, Search Retrieval Platform

We are building the next-generation search platform at Thomson Reuters, with a vision to democratize knowledge construction and consumption. The platform consolidates search systems across products and services, serving millions of end users.

As a Staff Software Engineer, you will build platform capabilities that let any product team, research team, or AI agent get high-quality, customizable search without building it from scratch. You will work closely with product, research engineering, and applied science partners.

Key Responsibilities:

• Turn customer and product needs into platform capabilities
• Design retrieval APIs and search services for end users, AI agents, and internal systems
• Make retrieval customizable by design with pluggable components
• Own content ingestion and indexing on the platform

Partner across disciplines:

• Partner with Research Engineering and Applied Science to take new retrieval techniques from experiment to supported platform capability
• Work with product and client teams to prioritize the roadmap and bring adoption feedback back into the platform

Quality, security, and trust:

• Build evaluation into the platform with offline evaluation, search grading, and online experiments
• Own the security posture of retrieval with multi-tenant data isolation and entitlements enforced at query time
• Own SLOs, observability, and on-call for what you build

Technical leadership:

• Set architectural direction and design patterns across retrieval and search services
• Lead cross-team initiatives and make hard trade-offs between near-term product needs and long-term platform goals
• Mentor Senior and mid-level engineers

Required Qualifications:

• 8+ years of software engineering experience with progression to staff-level scope
• Experience building search, retrieval, or data-intensive platforms at scale
• Track record translating customer needs into platform capabilities
• Experience designing APIs and services consumed by multiple teams
• Operational rigor with SLOs, observability, on-call ownership, and learning from incidents
• Fluency with AI-assisted development tools

Preferred qualifications:

• Production experience with search engines such as Vespa, OpenSearch, or Elasticsearch
• Experience with hybrid retrieval, ranking, and embedding pipelines
• Experience building tools or APIs for AI agents
• Search evaluation experience
• Multi-tenant authentication, authorization, and entitlements
• Experience operationalizing ML models in production

What Success Looks Like:

• In the first 90 days: Learn the platform's retrieval, indexing, and API architecture; build relationships with product, research, and client teams; take on-call ownership and deliver one meaningful improvement
• In the first year: Deliver a customizable retrieval capability; establish evaluation coverage, SLOs, and runbooks; move at least one research innovation from experiment to supported platform capability

What's in it For You:

• Hybrid Work Model
• Flexibility & Work-Life Balance
• Career Development and Growth
• Industry Competitive Benefits
• Culture
• Social Impact