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About This Role
Principal Technologist \- AI \& Data Center Networking
\#LI\-Remote
Location \- US Remote (travel to customers)
About 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.
The Role
DriveNets is seeking a Principal Technologist to be a senior technical leader within our Pre\-Sales organization. Join a dynamic and forward\-thinking company at the forefront of network transformation. We leverage advanced technologies to develop innovative solutions that drive efficiency, scalability, and exceptional network performance. Collaborate with the industry's best as we partner with hyperscalers, emerging NeoClouds, and enterprises building AI/HPC GPU fabrics, shaping the future of GPU Ethernet interconnect. 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 in complex pre\-sales cycles for AI networking and data center infrastructure. You will lead solution architecture and design for DriveNets' most strategic opportunities globally \- working independently or alongside Solutions Architects and Sales teams to translate customer business challenges 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.
Responsibilities
- Own the technical architecture for DriveNets' most complex and high\-value customer opportunities \- spanning AI/HPC GPU fabric design and large\-scale data center network infrastructure.
- 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 networking infrastructure.
- Lead proof\-of\-concept design and execution \- defining success criteria, driving benchmarking plans, and ensuring results are communicated with the rigor and clarity that wins technical confidence at the customer.
- Engage directly with engineering leads, network 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 from customer engagements and translating them into concrete requirements for DriveNets' Product Management and Engineering teams.
- Define and publish architecture playbooks, reference designs, and best practices 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.
- Represent DriveNets at industry events, author white papers and technical blogs, and build DriveNets' external technical brand in the AI networking space.
Requirements:
What we need to see:
- 12\+ years of experience in data center networking architecture and design, with at least 3 of those years focused on AI/HPC infrastructure or hyperscale environments.
- Extensive hands\-on depth in large\-scale switching and routing \- BGP, EVPN/VXLAN, QoS, lossless Ethernet (PFC, ECN), and network automation (NETCONF, YANG, REST APIs).
- 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 virtualization technologies at the x86 level \- including DPDK, SR\-IOV, and SmartNICs/DPUs \- and their role in accelerating network I/O and offloading data plane functions in AI and data center environments.
- Experience leading technical responses to RFP/RFQs and owning solution architecture documentation end\-to\-end.
- Experience with scripting and automation (Python, APIs, JSON) in the context of network 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 20%.
Ways to stand out from the crowd:
- Deep familiarity with AI/HPC networking \- RoCEv2, InfiniBand, GPU, NIC, DPU \- and how network design decisions directly impact AI workload performance.
- Experience with NCCL/RCCL tuning and GPU cluster benchmarking, and understanding of collective communication behavior at scale.
- Hands\-on knowledge of scale\-up (NVLink, UALink) and scale\-out (Enhanced Ethernet, UEC, InfiniBand) interconnect trade\-offs in production AI cluster environments.
- Familiarity with GPU resource scheduling (Slurm, Kubernetes) and how orchestration interacts with network design.
- Experience with observability and telemetry (Prometheus, Grafana, gNMI, OTLP) in large\-scale networking environments.
- Understanding of data center operations fundamentals \- power, cooling, rack design \- at hyperscale.
- CCIE, JNCIE, or equivalent \- advantage.
EDUCATION
BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or other Engineering fields, or equivalent experience.
DriveNets is an equal\-opportunity employer. We do not discriminate based on race, religion, color, national origin, sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a 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/
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Drivenets, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Drivenets AI Hiring
Drivenets has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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