Vinci
Member of Technical Staff, AI Compute & Data Infrastructure
$200K–$240K
Palo Alto HQ · Hybrid · Staff · Full-time · Posted Sep 15, 2026
- Experience
- 10+ years building large-scale distributed systems, including several years running GPU infrastructure for large model training.
- Education
- Advanced degree in computer science or a related field, or equivalent industry experience.
About the job
Own and build the compute, scheduling, storage, and training infrastructure that supports Vinci’s physics foundation models, while ensuring runs are reliable, fast, and cost-effective.
What you'll do
- Set GPU architecture and workload scheduling, and plan future capacity and investment.
- Build and operate petabyte-scale data infrastructure and the platform for launching, monitoring, and recovering training and validation runs.
- Write and review production code, debug infrastructure, improve reliability and utilization, and mentor and hire engineers.
What they ask for
- 10+ years building large-scale distributed systems, including several years running GPU infrastructure for large model training.
- Direct experience operating multi-node training on modern accelerators and serving training data at petabyte scale.
- Experience setting scheduling and quota policies for shared GPU capacity.
- Advanced degree in computer science or a related field, or equivalent industry experience.
Nice to have
- Kubernetes, Ray, Slurm, Airflow, or comparable orchestration running ML workloads in production.
- Hybrid or multi-cloud GPU capacity experience, including capacity planning and cost negotiation with providers.
Pay
$200K – $240K (USD, base salary)
Tools
Kubernetes, Ray, Slurm, Airflow, PyTorch distributed, FSDP, DeepSpeed
AI focus
Build and operate the GPU compute and petabyte-scale data infrastructure used to train and validate physics foundation models.
Model-written summary · the original posting is authoritative