Looking for a Senior Infrastructure Engineer to help build and evolve the systems that products and engineering teams depend on every day, covering cloud infrastructure, deployment, observability, reliability, networking, security, and developer tooling.
Responsibilities
Designing and evolving cloud infrastructure on GCP
Building reliable deployment and CI/CD systems
Improving observability across services, infrastructure, and production workloads
Building tooling that makes engineers faster and reduces operational overhead
Improving reliability, scalability, and resilience across systems
Requirements
Nice to have: Experience supporting data-intensive or machine learning systems
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.
Design, build, and maintain high-performance batch and real-time data pipelines that ingest, process, and deliver large datasets.
Responsibilities
Design, build, and maintain high-performance batch and real-time data pipelines
Identify opportunities to automate manual workflows, optimize query execution, and re-engineer infrastructure
Build production-grade ETL/ELT pipelines using Python, SQL, and cloud ecosystems
Develop, test, and maintain tailored tooling, data marts, and feature stores that enable analytics engineers and data scientists
Collaborate directly with cross-functional stakeholders, leading root cause analysis on pipeline bottlenecks, managing dependencies, and ensuring sound governance
