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The Company
DriveNets is a leader in high-scale networking software for AI infrastructure and service providers. The company pioneered a disaggregated networking architecture that transforms the economics of large-scale networks while maximizing performance, utilization, and operational efficiency. DriveNets-powered networks are deployed by global leaders, including AT&T and Comcast, supporting more than 30% of total U.S. internet traffic. DriveNets AI Fabric delivers full-stack networking for AI infrastructures, providing the highest-performance, Ethernet-based alternative to InfiniBand. The solution is deployed by hyperscalers, NeoClouds, and enterprises worldwide. With over $1B raised, DriveNets continues to push the boundaries of modern networking infrastructure. An empty job that is used for creating requisitions 'from scratch.
The Role
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.
What we need to see:
- 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.
If your experience is close but doesn’t fulfil all requirements, please apply. DriveNets is on a mission to build an innovative company comprised of individuals with different backgrounds, perspectives, and experiences.
DriveNets is an equal opportunity employer. We do not discriminate based on upon race, religion, national origin, sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with disability, or other applicable legally protected characteristics.
More about DriveNets
Based in Israel with locations in Romania, US, India and Japan as well as extended teams, DriveNets operations cover more than twelve countries. With recognition by industry analysts and through partnerships with market leaders such as AMD, Broadcom, Dell and others, DriveNets is pushing market momentum, delivering the scale and efficiency that modern AI workloads demand. Visit our website: https://drivenets.com/company/
Skills Required
- 12+ years of experience in data center compute infrastructure architecture and design
- At least 3 years focused on AI/HPC GPU or accelerator platforms and hyperscale environments
- Hands-on expertise in GPU/accelerator hardware and system design, including NVIDIA or AMD platforms
- Experience with compute cluster orchestration using Kubernetes and Slurm
- Experience with distributed training and inference frameworks
- Senior pre-sales, solutions architecture, or systems architecture experience
- 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
- Experience with scripting and automation using Python, APIs, and JSON
- Exceptional communication and presentation skills
- Willingness to travel domestically and internationally approximately 10%
- Familiarity with PyTorch, TensorFlow, or JAX
- Experience with NCCL or RCCL tuning and GPU cluster benchmarking such as MLPerf
- Knowledge of NVLink and UALink scale-up interconnect trade-offs
- Familiarity with GPU scheduling and orchestration using Slurm, Kubernetes, or Ray
- Experience with GPU observability and telemetry using DCGM, Prometheus, or Grafana
- Understanding of data center power, cooling, and rack design for high-density GPU compute
- NVIDIA or AMD platform certifications, or equivalent
- BS, MS, or PhD in Electrical Engineering, Computer Engineering, Computer Science, Physics, or another engineering field, or equivalent experience
What We Do
DriveNets is a rapidly growing software company that has created a radical new way for service providers and hyperscalers to build their networking infrastructure. DriveNets Network Cloud and DriveNets Network Cloud-AI are new innovative networking solutions that apply the cloud architectural approach to high-scale networking. They bring together the scalability of standard Ethernet Clos architecture with the high performance and reliability of service provider networking, delivering optimal networking performance, scale and cost structure for service providers and hyperscalers. Founded by Ido Susan and Hillel Kobrinsky, two successful telco entrepreneurs, DriveNets Network Cloud is the leading open disaggregated networking solution based on cloud-native software running over standard white boxes. Over three funding rounds, DriveNets raised $587 million. Its solutions are used by tens of service providers globally and are in proof-of-concept and lab trials at dozens of operators and hyperscalers, consistently ranking #1 in trials for breadth of capabilities and solution quality. AT&T, the largest backbone in the US, deployed DriveNets Network Cloud across its core network, and DriveNets is currently transporting more than 52% of AT&T’s core network traffic. DriveNets is engaged with over 100 Tier-1 operators and cloud-providers on large projects in North America, Asia and Europe.
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