Lead technical design, build, and demonstration of scalable AI training solutions on NVIDIA GPU hardware and software for enterprise customers.
Compensation
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
Work with Cloud Service Providers to design, build, and demonstrate scalable AI training solutions using NVIDIA hardware and software.
Lead technical engagements across foundation model pre-training and post-training, helping customers move from architecture and proofs of concept to production adoption.
Develop and optimize end-to-end training workflows spanning data preparation, distributed training, checkpointing, and evaluation, including reinforcement learning pipelines.
Build customer-specific proofs of concept that address critical technical and business needs, demonstrating improvements in training performance, reliability, and efficiency.
Help customers diagnose and resolve bottlenecks across GPU compute, memory, communication, networking, storage, and workload orchestration.
Requirements
BS, MS, or PhD in Computer Science, Electrical or Computer Engineering, Statistics, Physics, or a related technical field, or equivalent experience.
Nice to have: reinforcement learning from human feedback
Senior Solutions Architect to design, build, and optimize AI training solutions using NVIDIA GPU hardware and software on cloud platforms.
Compensation
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
Work with Cloud Service Providers to design, build, and demonstrate scalable AI training solutions using NVIDIA hardware and software.
Lead technical engagements across foundation model pre-training and post-training, helping customers move from architecture and proofs of concept to production adoption.
Develop and optimize end-to-end training workflows spanning data preparation, distributed training, checkpointing, and evaluation, including reinforcement learning pipelines.
Build customer-specific proofs of concept that address critical technical and business needs, demonstrating improvements in training performance, reliability, and efficiency.
Help customers diagnose and resolve bottlenecks across GPU compute, memory, communication, networking, storage, and workload orchestration.
Requirements
BS, MS, or PhD in Computer Science, Electrical or Computer Engineering, Statistics, Physics, or a related technical field, or equivalent experience.
Nice to have: NVIDIA NeMo Framework
Nice to have: Megatron Core
Postdoctoral Appointee - Federated Learning and Foundation Model
A research position focused on optimization and training dynamics for large-scale machine learning, specifically within distributed and federated settings and foundation models.
Compensation
The expected hiring range for this position is $72,879.00-$121,465.00.
Responsibilities
Conducts research on optimization and training dynamics for large-scale machine learning, combining analysis with careful experiments.
Designs, implements and benchmarks new training algorithms for distributed and federated settings, with attention to efficiency, privacy and robustness.
Contributes research software to open-source projects and keeps experiments reproducible.
Publishes and presents results at leading conferences and journals, and contributes to program reports.
Works with collaborators across institutions.
Requirements
Ph.D. (completed within the last 0–5 years) in optimization for machine learning, deep learning theory, or distributed or federated learning
Senior AI System Engineer - LLM Training SystemHuawei Technologies Canada Co., Ltd. · Vancouver, Canada