About DriveNets
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.
We are seeking a senior ATE Software Engineer to design, build, and deploy our next-generation automated manufacturing test platform for advanced telecom, high-density switching, and optical networking hardware.
In this role, you will bridge the gap between R&D hardware/firmware teams and high-volume manufacturing lines. You will take ownership of ingesting, refactoring, and consolidating disparate R&D scripts, diagnostic tools, and legacy software into a single, scalable, production-grade test platform.
We expect you to leverage modern AI-assisted development tools and workflows to maximize engineering efficiency, accelerate delivery timelines, and build highly robust, self-healing test automation. You will deploy test stations across global Contract Manufacturers (EMS/ODM), actively stabilize production lines through bring-up and ramp-up, and drive continuous test maintenance and root-cause failure analysis (RCA).
עיקרי התפקיד
Test Platform Architecture & Software Development:
Design, develop, and productize robust, modular test software frameworks for board-level (PCBA) and system-level manufacturing testing across multiple product lines and manufacturing sites.
Codebase Unification & Refactoring:
Consolidate and standardize R&D diagnostic libraries, silicon validation scripts, and firmware tools developed across various environments (Python, C# / Visual Studio, VB, ATEasy, Linux) into a unified, maintainable test execution engine.
AI-Accelerated Development & Workflow Automation:
Actively integrate Generative AI tools and AI coding agents into your daily workflow to accelerate code conversion/refactoring, automate test case synthesis, generate edge-case validation scripts, and enhance overall codebase resilience.
Production Line Deployment & Stabilization:
Deploy test stations and fixtures to local and overseas EMS/CM production lines. Monitor, debug, and fine-tune test execution during NPI and ramp-up phases until yield, takt time, and test stability targets (CPK / First Pass Yield) are achieved.
Failure Investigation & Intelligent RCA:
Investigate complex system-level and component-level test failures. Utilize advanced log analysis, structured telemetry, and pattern recognition to drive root-cause investigations and implement preventive software/hardware fixes.
Ongoing Test Maintenance & Optimization:
Provide continuous operational Production line support and maintenance, bug fixes, software updates, and station cycle-time optimizations for active mass-production lines.
R&D Collaboration & Test Specification:
Collaborate closely with Hardware, Firmware, and Validation teams during early design phases to define test specifications, drive Design for Testability,
Documentation & Technical Reporting:
Author and maintain comprehensive testing documentation, Standard Operating Procedures (SOPs), test coverage matrices, and detailed failure analysis reports.
Manufacturing Systems Integration:
Integrate automated test sequences with CM’s SFC/MES/QMS, serialized tracking, and central test log databases.
Log Analysis & Analytics Tool Development:
Design and build dedicated log parsing, data visualization, and diagnostic analytics tools to streamline complex hardware/software debugging, accelerate root-cause analysis (RCA), and drive continuous production line cycle-time and yield optimization.
דרישות
B.Sc. in Electrical Engineering, Software Engineering, Computer Science, or equivalent practical experience.
5+ years of hands-on experience in ATE software development and production test engineering for complex telecom, networking, or optical hardware products.
Advanced, everyday practical mastery of AI-assisted engineering tools (e.g., GitHub Copilot, Cursor, LLM-based coding agents, automated prompt-driven refactoring) to radically accelerate code delivery, debug legacy systems, and enforce code quality.
Strong software engineering skills in Python and Linux environments.
Practical experience with test development in C# (.NET / Visual Studio), VB, or specialized test sequencers such as ATEasy.
Proven track record of deploying, commissioning, and stabilizing ATE test benches at overseas tier-1 EMS/CM facilities.
Strong grasp of digital and communication protocols (PCIe, I2C, SPI, MDIO, UART, Ethernet) and familiarity with lab equipment (oscilloscopes, power supplies, high-speed traffic generators).
Strong analytical and debugging skills to isolate issues between hardware defects, firmware bugs, test software, and fixture/cabling failures.
Experience writing clear engineering documentation, test procedures (SOPs), and technical root-cause reports.
Excellent verbal and written English communication skills; readiness to support global manufacturing sites (including occasional international travel for line bring-up).
Proven hands-on experience integrating ATE platforms and automated test tools with Contract Manufacturers MES, QMS, and Shop Floor Control (SFC) systems for automated serialization, unit routing, and quality data logging.
יתרון
Experience with high-density Ethernet switches, data center networking architectures.
Hands-on experience testing high-speed optical transceivers and SerDes interfaces.
Strong advantage: Practical knowledge of high-speed SerDes configuration, link-tuning, and signal integrity validation.
Experience implementing AI/ML-driven log parsers, anomaly detection, or predictive yield analytics for manufacturing telemetry.