Build and scale training libraries and infrastructure for autonomous driving models, enabling training on thousands of GPUs and massive datasets.
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: 224,000 USD – 356,500 USD for Level 5; and 272,000 USD – 431,250 USD for Level 6.
Responsibilities
Crafting, scaling, and hardening deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.
Improving efficiency throughout the training stack: data loaders, distributed training, scheduling, and performance monitoring.
Building robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.
Collaborating with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.
Owning core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.
Build and scale deep learning infrastructure libraries and frameworks for autonomous driving models.
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: 224,000 USD – 356,500 USD for Level 5; and 272,000 USD – 431,250 USD for Level 6.
Responsibilities
Crafting, scaling, and hardening deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.
Improving efficiency throughout the training stack: data loaders, distributed training, scheduling, and performance monitoring.
Building robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.
Collaborating with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.
Owning core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.
Own the commercial and technical strategy for net new customer wins in HCLS AI, translating customer requirements into product feedback and developing deal structures to accelerate opportunities.
Locations (2)
New York, New York, United States
San Francisco, California, United States
Compensation
The base salary range for this role is $177,000 to $237,000.
Responsibilities
Own the commercial and technical strategy for net new customer wins in HCLS AI
Drive new business opportunities at pharmaceutical companies, techbio and biotech firms, medical device makers, payers, and health systems
Build deep expertise across the HCLS AI stack and translate customer requirements into product feedback
Develop deal structures, technical playbooks, and benchmark narratives
Engage directly with heads of computational science, AI platform leads, CIOs, and research IT
Essential Skills
High-Performance Storage:
Parallel Filesystems: Hands-on operational experience with at least one major AI storage platform: VAST Data, Weka.io, DDN Lustre (Exascaler), or IBM GPFS (Spectrum Scale).
Linux I/O Stack: Deep understanding of the Linux VFS (Virtual File System), block devices, and how to debug I/O performance using tools like iostat, iotop, and strace.
RDMA Storage: Experience configuring NVMe-over-Fabrics (NVMe-oF) or NFS-over-RDMA, understanding the dependency on the underlying InfiniBand/RoCE network.
Automation & Containerisation:
Ansible Storage: Proficiency in writing Ansible playbooks to automate the installation of storage clients and configuration of mount points.
Kubernetes Storage: Understanding of StorageClasses, PVCs, and how to debug CSI Driver pods (checking logs for mount failures).
GPUDirect: Conceptual understanding of NVIDIA GPUDirect Storage (GDS) and the ability to verify if GDS is active.
Desirable Experience
Vendor Specifics: Deep certification or experience with Pure Storage (FlashBlade) or NetApp ONTAP AI configurations.
Object Storage: Experience interacting with S3-compatible object stores via CLI for model weight retrieval.
Data Migration: Experience using tools like fpsync or rclone to move petabyte-scale datasets between tiers.
Certifications
Highly Desirable:
NVIDIA-Certified Associate: AI Infrastructure and Operations (NCA-AIIO)
Vendor Certifications:
VAST Certified Administrator (VCP-AD1)
WEKA Technical Xpert Certification
Red Hat Certified Specialist in Storage Administration
Success Metrics (KPIs)
I/O Performance: Achieving >95% of the theoretical line-rate throughput on IOR/FIO benchmarks for provisioned clients.
Mount Stability: Zero "Stale File Handles" or disconnected mounts across the cluster during the 72-hour burn-in period.
Ticket Velocity: Consistently meeting SLAs for storage-related support tickets.
#LI-RB1
Advanced technical degree (MS or PhD in Computational Biology, Bioinformatics, Computer Science, or equivalent) or MBA with a strong technical foundation in AI/ML systems
Nice to have: Direct experience selling into leading HCLS organizations
Nice to have: Familiarity with NVIDIA's healthcare and life sciences ecosystem
Nice to have: Background supporting large-scale scientific computing
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