# Staff Inference Engineer - AI at Thomson Reuters

Thomson Reuters is hiring a Staff Inference Engineer - AI. It pays $127k-$237k a year and Level rates it Builds AI ●●●●; you can [apply on Thomson Reuters](https://jobsbylevel.com/go/4f1abfaf-664a-4695-b38d-3f7beaed0075?utm_medium=ai-agent).

AI Level 4, AI centrality 90 out of 100. United States of America, Eagan, Minnesota.

## Details

- Company: [Thomson Reuters](https://jobsbylevel.com/companies/thomson-reuters)
- AI level: AI Level 4 (score 90 out of 100)
- Location: United States of America, Eagan, Minnesota
- Salary: $127k-$237k
- Posted: October 23, 2026
- Apply: https://jobsbylevel.com/go/4f1abfaf-664a-4695-b38d-3f7beaed0075?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=staff-inference-engineer-ai-at-thomson-reuters-d815c7

## Prepare for this job

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## Description

Job Description Thomson Reuters is seeking a Staff Inference Engineer, AI. This person will collaborate with platform teams to enhance capacity forecasting for AI workloads and work with Product, Data Science, Architecture, and Enterprise AI teams to onboard new research models into production. Key Responsibilities Within Platform Engineering and Enterprise AI Services, an AI Inference Engineer is responsible for productionizing, optimizing, and scaling AI and LLM workloads that power TR’s AI driven products. This role ensures that our trained models—from classical ML to generative AI—run efficiently across TR’s multi cloud footprint (AWS, Azure, GCP, OCI), meet strict enterprise reliability requirements, and integrate seamlessly with our data backbone (Snowflake, OpenSearch vector search, API managed model routing). The successful candidate will help build the next generation of TR’s AI infrastructure, working alongside cloud engineering, data engineering, product teams, and AI Services. Optimize LLMs and ML models for high-performance inference using techniques such as quantization, pruning, distillation, and hardware specific tuning Deploy and scale inference workloads on GPUs across AWS, Azure, GCP and internal Kubernetes clusters, ensuring predictable performance during peak traffic hours, especially during business hours Implement routing and failover strategies for OpenAI/Anthropic/Vertex AI traffic Integrate models into production grade APIs supporting TR products and enterprise workflows. Develop highly optimized environment and eliminate performance bottlenecks to reduce latency. Collaborate with Platform Engineering teams (Landing Zones, Network, Storage, Compute, AI) to ensure inference workloads align with TR’s cloud native patterns (AWS, Azure, GCP, OCI) Build and optimize containerized inference pipelines using Kubernetes for large-scale distributed workloads Ensure compliance with TR’s AI standards for deployment, monitoring, governance, and drift detection Profile inference performance, identify GPU/CPU bottlenecks, and optimize compute utilization across heterogeneous hardware Implement observability and health monitoring for inference pipelines, ensuring reliability of enterprise AI services Collaborates closely with AI engineers to invent new quantization techniques, improve numerical precision, and explore non‑standard architectures, and support the scale out of AI infrastructure during critical releases and global product rollouts Partner with Cloud Engineers (Azure, AWS, GCP) to develop guardrails and automation that support inference workloads Required Qualifications 7+ years of relevant experience Strong understanding of ML/LLM fundamentals and inference optimization techniques. Hands-on experience with GPU programming (CUDA preferred), inference runtimes (TensorRT, ONNX Runtime), and deep learning frameworks (PyTorch/TensorFlow) Proficiency in Python and at least one systems language (C++ strongly preferred for performance critical inference paths) Experience deploying AI workloads to AWS/GCP/Azure and Kubernetes Preferred Qualifications Familiarity with vector search systems (OpenSearch vectors) and retrieval augmented generation pipelines Knowledge of distributed systems, microservices, CI/CD, and cloud native architecture #LI-MW1 New Position: This position is open due to an existing vacancy to support our evolving business needs. What’s in it For You? Hybrid Work Model: We’ve adopted a flexible hybrid working environment for our office-based roles while delivering a seamless experience that is digitally and physically connected. Flexibility & Work-Life Balance: Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities, whether caring for family, giving back to the community, or finding time to refresh and reset. This builds upon our flexible work arrangements, including work from anywhere for up to 8 weeks per year, empowering

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