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.
The successful candidate will be responsible for productionizing, optimizing, and scaling AI and LLM workloads that power TR's AI-driven products. They will ensure that trained models run efficiently across TR's multi-cloud footprint (AWS, Azure, GCP, OCI), meet strict enterprise reliability requirements, and integrate seamlessly with the data backbone (Snowflake, OpenSearch vector search, API managed model routing).
Key responsibilities:
- 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
- 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 environments and eliminate performance bottlenecks to reduce latency
- Collaborate with Platform Engineering teams to ensure inference workloads align with TR's cloud-native patterns
- 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
- Collaborate with AI engineers to invent new quantization techniques and improve numerical precision
Requirements:
- 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)
- Experience deploying AI workloads to AWS/GCP/Azure and Kubernetes
Benefits:
- Hybrid work model
- Flexibility and work-life balance
- Career development and growth opportunities
- Industry-competitive benefits
- Culture of inclusion and belonging
- Social impact initiatives
Salary range: $127,400 USD - $236,600 USD (for eligible US locations)