Principal Technologist - AI Compute

Drivenets•Middletown, NJ

7d•Remote

About The Position

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.

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%.

Nice To Haves

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.

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.