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Staff Inference Engineer – AI
Thomson Reuters. Collaborate with platform teams to enhance capacity forecasting for AI workloads .
Posted 10/23/2026full-timeEagan • Minnesota • United StatesLead💰 $127,400 - $236,600 per yearWebsite
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive experience in optimizing and deploying AI and LLM workloads across cloud platforms, with a strong focus on performance optimization and compliance with AI governance standards. Proficient in integrating machine learning models into production-grade APIs and developing containerized inference pipelines using Kubernetes.
Highest-signal resume keywords
ML/LLM FundamentalsGPU ProgrammingInference Optimization TechniquesDeep Learning FrameworksCloud Deployment Experience
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonC++CUDATensorRTONNX RuntimePyTorchTensorFlowQuantization TechniquesPerformance OptimizationInference Performance Profiling
Tools & Technologies
AWSGCPAzureKubernetesOpenSearchAPI ManagementCloud-Native ArchitectureDistributed SystemsMicroservicesCI/CD
Industry Keywords
AI WorkloadsInference PipelinesGovernance StandardsLatency ReductionModel Integration
Tech Stack
Tools & technologiesAWSAzureCloudDistributed SystemsGoogle Cloud PlatformKubernetesMicroservicesPythonPyTorchTensorflowC++
About the role
Key responsibilities & impact- Collaborate with platform teams to enhance capacity forecasting for AI workloads
- Work with Product, Data Science, Architecture, and Enterprise AI teams to onboard research models into production
- Productionize, optimize, and scale AI and LLM workloads powering Thomson Reuters products
- Run trained classical ML and generative AI models efficiently across AWS, Azure, GCP, and OCI
- Integrate models with Snowflake, OpenSearch vector search, and API-managed model routing
- Optimize LLMs and ML models using quantization, pruning, distillation, and hardware-specific tuning
- Deploy and scale GPU inference workloads across cloud platforms and internal Kubernetes clusters
- Implement routing and failover strategies for OpenAI, Anthropic, and Vertex AI traffic
- Integrate models into production-grade APIs supporting products and enterprise workflows
- Develop highly optimized environments and eliminate performance bottlenecks to reduce latency
- Collaborate with Platform Engineering teams on cloud-native inference patterns
- Build and optimize containerized inference pipelines using Kubernetes for distributed workloads
- Ensure compliance with AI deployment, monitoring, governance, and drift-detection standards
- Profile inference performance and optimize GPU/CPU utilization across heterogeneous hardware
- Implement observability and health monitoring for inference pipelines
- Develop quantization techniques, improve numerical precision, and explore non-standard architectures
- Support AI infrastructure scale-out during critical releases and global product rollouts
- Partner with Cloud Engineers to develop guardrails and automation for inference workloads
Requirements
What you’ll need- 7+ years of relevant experience
- Strong understanding of ML/LLM fundamentals and inference optimization techniques
- Hands-on experience with GPU programming; CUDA preferred
- Experience with inference runtimes: TensorRT and ONNX Runtime
- Experience with deep learning frameworks: PyTorch and TensorFlow
- Proficiency in Python
- Proficiency in at least one systems language; C++ strongly preferred for performance-critical inference paths
- Experience deploying AI workloads to AWS, GCP, Azure, and Kubernetes
- Preferred: familiarity with vector search systems, including OpenSearch vectors
- Preferred: knowledge of retrieval augmented generation pipelines
- Preferred: knowledge of distributed systems, microservices, CI/CD, and cloud native architecture
Benefits
Comp & perks- Flexible hybrid working environment
- Work from anywhere for up to 8 weeks per year
- Flexible vacation
- Two company-wide Mental Health Days off
- Headspace app access
- Retirement savings
- Tuition reimbursement
- Employee incentive programs
- Mental, physical, and financial wellbeing resources
- Two paid volunteer days off annually
- Pro-bono consulting projects and ESG initiatives
- United States: health, dental, vision, disability, and life insurance
- United States: competitive 401(k) plan with company match
- United States: sick and safe paid time off
- United States: paid holidays
- United States: parental leave
- United States: sabbatical leave
- United States: optional hospital, accident and sickness insurance
- United States: optional life and AD&D insurance
- United States: Flexible Spending and Health Savings Accounts
- United States: fitness reimbursement
- United States: Employee Assistance Program
- United States: Group Legal Identity Theft Protection benefit
- United States: 529 Plan access
- United States: commuter benefits
- United States: Adoption & Surrogacy Assistance
- United States: Employee Stock Purchase Plan
- Annual Bonus eligibility based on enterprise and individual performance