[Hiring] Principal Technologist @DRIVENETS

Role Description

DriveNets is seeking a Principal Technologist to be a senior technical leader within our Pre-Sales organization, focused on the compute side of AI infrastructure. Join a dynamic and forward-thinking company at the forefront of AI infrastructure transformation. We partner with hyperscalers, emerging NeoClouds, and enterprises building AI/HPC GPU compute clusters, helping them get maximum performance and utilization out of their compute platforms. Our environment fosters creativity, teamwork, and growth, and offers you the opportunity to make a meaningful impact on multi-million-dollar customer engagements.

As a Principal Technologist, you will serve as the senior technical authority on AI compute infrastructure in complex pre-sales cycles. You will lead solution design for DriveNets' most strategic opportunities globally – working independently or alongside Solutions Architects and Sales teams to translate customer compute and workload requirements into scalable, differentiated technical solutions. You will engage at the C-level and VP level with hyperscalers, NeoClouds, service providers, and large enterprises, and will serve as a thought leader and enabler both internally and externally on GPU/accelerator compute architecture.

Responsibilities

• Own the technical architecture for DriveNets' most complex and high-value customer opportunities – spanning AI/HPC GPU and accelerator compute cluster design, workload performance, and the compute-to-network interface.
• Partner with Sales and Solutions Architects from early discovery through deal closure, establishing DriveNets as the technically superior choice for customers building next-generation AI compute infrastructure.
• Lead proof-of-concept design and execution – defining success criteria, driving GPU cluster benchmarking plans (training/inference throughput, scaling efficiency), and ensuring results are communicated with the rigor and clarity that wins technical confidence at the customer.
• Engage directly with ML infrastructure leads, compute architects, and C-level stakeholders at hyperscalers, NeoClouds, and large enterprises – building relationships that outlast any single deal.
• Serve as a product feedback engine – capturing deep field insights on GPU/accelerator platform trends and workload behavior, translating them into concrete requirements for DriveNets' Product Management and Engineering teams.
• Define and publish architecture playbooks, reference designs, and best practices for AI compute infrastructure that scale DriveNets' technical go-to-market across the Solutions Architect and Solutions Engineer community.
• Lead and mentor Solutions Architects and Solutions Engineers, raising the overall technical bar of the pre-sales organization on compute and workload topics.
• Represent DriveNets at industry events, author white papers and technical blogs, and build DriveNets' external technical brand in the AI compute infrastructure space.

Requirements

• 12+ years of experience in data center compute infrastructure architecture and design, with at least 3 of those years focused on AI/HPC GPU or accelerator platforms and hyperscale environments.
• Extensive hands-on depth in GPU/accelerator compute architecture – GPU/accelerator hardware and system design (NVIDIA/AMD), compute cluster orchestration (Kubernetes, Slurm), and distributed training/inference frameworks.
• Proven track record in senior pre-sales, solutions architecture, or system architecture roles, including direct experience influencing large, complex deals with VP and C-level stakeholders.
• Experience with GPU virtualization and partitioning – including MIG/vGPU, containerized ML workloads, and bare-metal GPU provisioning – and their role in maximizing compute utilization in AI environments.
• Experience with scripting and automation (Python, APIs, JSON) in the context of ML infrastructure operations and solution integration.
• Exceptional communication and presentation skills – able to command a room of engineers and a room of C-suite executives with equal credibility.
• Willingness to travel domestic and international approximately 10%.

Ways to stand out from the crowd

• Deep familiarity with AI/ML frameworks (PyTorch, TensorFlow, JAX) and how model architecture and training/inference patterns drive compute cluster requirements.
• Experience with NCCL/RCCL tuning and GPU cluster benchmarking (e.g., MLPerf), and understanding of collective communication behavior at scale.
• Hands-on knowledge of scale-up (NVLink, UALink) interconnect trade-offs and how they interact with scale-out network design in production AI cluster environments.
• Familiarity with GPU resource scheduling and orchestration (Slurm, Kubernetes, Ray) and how it interacts with compute cluster design and multi-tenant utilization.
• Experience with GPU observability and telemetry (DCGM, Prometheus, Grafana) in large-scale AI compute environments.
• Understanding of data center operations fundamentals – power, cooling, and rack design – as they relate to high-density GPU compute at hyperscale.
• NVIDIA/AMD platform certifications, or equivalent – advantage.

Education

• BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or other Engineering fields, or equivalent experience.