QA Engineer, E2E

DriveNets

. Design, create and execute comprehensive end-to-end testing scenarios for ACO based on requirements and design documents .

Posted 10/14/2026full-timeRa'anana • IsraelMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in application testing, particularly in UI/UX and database-backed workflows, with a strong focus on AI networking and performance benchmarking. Proficient in QA automation, cloud deployments, and collaboration within Agile environments.

Highest-signal resume keywords

Application TestingNetworking Knowledge (CCNP Level)QA Automation (Python, pytest, Playwright/Selenium)AI Workloads Optimization (NVIDIA/AMD)Cloud Deployments (AWS, Azure, GCP)

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Hard Skills

Application TestingNetworking KnowledgeQA AutomationAI Workloads OptimizationCloud DeploymentsSQL QueryingLinux EnvironmentsPerformance AnalysisBenchmarking MethodologiesTest Design

Soft Skills

Excellent Interpersonal SkillsStrong Communication SkillsSelf-StarterCollaborative MindsetAbility to Manage Multiple Priorities

Tools & Technologies

KubernetesSLURMAWSAzureGCPPostgreSQLPrometheusGrafanaGNMIAI Agents

Certifications & Qualifications

CCNP Level Networking Knowledge

Industry Keywords

AI NetworkingData-Center FabricsHigh-Performance NetworkingCollective Communication TechnologiesMulti-Tenancy

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformGrafanaKubernetesLinuxPostgresPrometheusPythonSeleniumSQLSwitching

About the role

Key responsibilities & impact

Design, create and execute comprehensive end-to-end testing scenarios for ACO based on requirements and design documents
Test ACO across UI/UX, REST APIs, database-backed workflows and managed infrastructure
Test switch provisioning and upgrades, inventory and topology, telemetry, alerts, configuration compliance, benchmarking and multi-tenancy
Maintain functionality across QA environments including AI fabric topologies, GPU servers, NetBox/SLURM/Kubernetes integrations and ACO deployments
Validate network connectivity, configuration and performance across AI cluster infrastructure
Verify BGP routing functionality and EVPN/VXLAN-based isolation on data-center fabrics
Execute and analyze AI/HPC benchmarks on GPU and CPU clusters through ACO's benchmarking engine and compare performance against baselines
Review benchmarking methodologies and test procedures for high-performance networking and collective communication technologies
Support telemetry collection and validate gNMI/OpenConfig, syslog, GPU and NIC metrics against device state
Use performance analysis tools, benchmarking frameworks and diagnostic utilities to execute tests, analyze results and troubleshoot issues
Execute regression tests for software updates, configuration changes and platform upgrades
Collaborate with development teams to investigate failures, reproduce defects, verify fixes and ensure system quality
Document test procedures, execution results, defects and performance metrics; prepare QA reports
Use AI agents to accelerate test design, execution, evidence collection and reporting, and help grow test automation

Requirements

What you’ll need

Minimum 3 years of application testing across UI/UX, database-backed data and multi-step workflows in web-based systems, including test design from detailed requirements (STP/STD/STR)
Networking knowledge at CCNP level or higher: L2/L3 switching and routing, BGP, VLAN/VXLAN/EVPN, MTU/MSS, ECMP, and troubleshooting with packet captures and device CLI
AI networking knowledge: data-center fabrics for AI workloads, leaf-spine and rail-optimized Clos designs, RoCEv2/RDMA, lossless Ethernet (PFC/ECN/DCQCN), 400/800G/1.6T optics and link health
Experience running, monitoring and optimizing AI workloads and benchmarks (NCCL/RCCL, RDMA, MPI, training and inference) on NVIDIA/AMD GPU servers, with schedulers such as SLURM or Kubernetes
Strong hands-on experience in Linux environments (CLI, networking stack, services, logs, scripting)
Hands-on experience working with AI agents: agent skills, rules/instruction (markdown) files, MCP servers and tools, and prompt-driven workflows (e.g., Claude Code, Codex, Cursor)
Hands-on experience in Agile work processes
QA automation experience with Python, pytest, Playwright/Selenium, REST API testing and CI pipelines
Kubernetes and containers experience, including kubectl, Helm, Lens/OpenLens and Argo CD
Cloud deployments experience with AWS, Azure or GCP, such as EKS
SQL querying and data validation, particularly PostgreSQL
Familiarity with DNOS, SONiC, Arista EOS, Junos, Prometheus/Mimir, Grafana/Perses, gNMI, Spirent or Ixia
Excellent interpersonal and communication skills
High-level proficiency in English, both written and verbal, and clear technical writing
Self-starter who learns quickly and adapts easily to new technologies
Ability to work under tight deadlines and manage multiple priorities
Strong team player with a collaborative mindset

Benefits

Comp & perks

Hands-on work with state-of-the-art AI infrastructure: 800G/1.6T Ethernet fabrics, NVIDIA and AMD GPU clusters, RoCE and collective communication
Dynamic and supportive work environment
AI-native engineering culture where agents are part of the daily workflow
Collaboration with a world-class team