Lead the development of LLM inference infrastructure and architectural direction of large-scale ML training and real-time inference systems for Amazon Ads.
Locations (3)
Seattle, Washington, United States
Arlington, Virginia, United States
New York, New York, United States
Compensation
USA, CA, Palo Alto - 193,300.00 - 261,500.00 USD annually
Responsibilities
Lead the development of LLM inference infrastructure for next-generation GenAI workloads at Amazon Ads scale
Own the architectural direction of large-scale ML training and real-time inference systems
Partner with applied scientists to design and optimize end-to-end ML pipelines
Influence technical direction beyond your team by establishing ML infrastructure best practices
Make principled tradeoff decisions on complex, ambiguous problems
Requirements
Bachelor's degree in computer science or equivalent
Nice to have: full software development life cycle
health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
Lead complex, multi-disciplinary projects to design and deliver automated software solutions supporting Google’s data center infrastructure capacity expansion.
Join an early-stage AI infrastructure team as its founding cloud infrastructure engineer. You will build the production cloud foundation for a platform that helps organizations provide reliable, governed context to AI systems.
Compensation
Visa sponsorship is available.
Responsibilities
Design and build cloud infrastructure from the ground up, taking systems from initial architecture through deployment and scale.
Make foundational decisions about cloud architecture and establish infrastructure standards and best practices.
Build and operate Kubernetes-based infrastructure and infrastructure-as-code solutions.
Develop CI/CD pipelines, deployment automation, monitoring, observability, and logging systems.
Design and maintain secure cloud networking, access controls, and database infrastructure.
Partner with AI-native founders and engineering teams to design, optimize, and scale AI platforms on NVIDIA technologies, focusing on agentic systems, AI copilots, and multimodal AI.
Compensation
expected base salary ranges for this position are: 152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.
Responsibilities
Serve as a trusted technical advisor to the next generation of AI-native companies building category-defining applications such as developer copilots, AI search engines, agentic platforms, and multimodal creative tools.
Work directly with startup founders and engineering teams to architect and optimize AI workloads using NVIDIA technologies including CUDA-X libraries, TensorRT-LLM, Triton Inference Server, NVIDIA NeMo, NIM microservices, and GPU-accelerated data processing frameworks.
Help AI-native companies scale training and inference infrastructure, optimizing model performance, cost efficiency, and latency across NVIDIA accelerated computing platforms.
Guide startups through complex architectural decisions including model optimization, inference scaling, agent frameworks, multimodal pipelines, and real-time AI systems.
Collaborate closely with NVIDIA engineering, research, product, and go-to-market teams to identify emerging AI-native categories and influence NVIDIA’s platform roadmap.
Requirements
BS/MS degree or equivalent experience in Computer Science, Engineering, or a related field
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.
Responsibilities
Serve as a trusted technical advisor to the next generation of AI-native companies building category-defining applications such as developer copilots, AI search engines, agentic platforms, and multimodal creative tools.
Work directly with startup founders and engineering teams to architect and optimize AI workloads using NVIDIA technologies including CUDA-X libraries, TensorRT-LLM, Triton Inference Server, NVIDIA NeMo, NIM microservices, and GPU-accelerated data processing frameworks.
Help AI-native companies scale training and inference infrastructure, optimizing model performance, cost efficiency, and latency across NVIDIA accelerated computing platforms.
Guide startups through complex architectural decisions including model optimization, inference scaling, agent frameworks, multimodal pipelines, and real-time AI systems.
Collaborate closely with NVIDIA engineering, research, product, and go-to-market teams to identify emerging AI-native categories and influence NVIDIA’s platform roadmap.
Requirements
BS/MS degree or equivalent experience in Computer Science, Engineering, or a related field
Nice to have: building or scaling AI-powered products
Sr. Machine Learning EngineerAmazon · Seattle, US+2 more
Technical Program Manager III, Infrastructure Deployment Software NPI, Google CloudGoogle · Sunnyvale, US+1 more
Founding Cloud Infrastructure EngineerClera-AI · San Mateo, US