nEye.ai

Software Engineer, Test Systems

Santa Clara, CA

Sponsorship not specified$160k-$210kDetected 256 days ago

PythonC++GitDockerCI/CDData EngineeringLLMsFPGAElectrical EngineeringLabVIEWpytestTest AutomationCommunication

About the role

This role is cross-functional, highly collaborative, and central to scaling test capability from bench-level exploration to high-throughput reliability and performance validation.
These tools assist our recruitment team but do not replace human judgment.
Final hiring decisions are ultimately made by humans.

Develop and maintain station control software for optical/electrical/MEMS test platforms (e.g., Python/C++/LabVIEW or similar).
Implement instrument drivers for lasers, detectors, motion controllers, FPGA boards, MEMS control modules, etc.
Own data acquisition pipelines (from sensor readout → structured test results → database/logging).
Design modular test frameworks that support rapid test case development for validation & reliability workflows.
Collaborate with hardware, optical, MEMS, firmware, and validation teams to define and translate station requirements.
Develop UI or scripting interfaces that enable validation engineers to configure and run experiments confidently.
Support failure analysis by instrumenting stations for deeper debug modes and data capture.
Experience developing Python/C++ control software interfacing with hardware.
Create comprehensive written documentation, including software process flows, SOPs, and work instructions.

5-7+ years writing software for lab equipment, automated test setups, semiconductor/optics validation, or hardware-in-the-loop systems.

Prior experience in logging, configuration management, and structured result visualization.
Comfortable partnering with hardware engineers and systems validation engineers.
Excellent written and verbal communication skills, with the ability to present complex technical findings clearly to both experts and non-experts.
Deep familiarity using AI tools (e.g., LLMs, copilots) to accelerate engineering workflows, debugging, and problem-solving.
Exposure to hands-on testing and validation of silicon photonics, MEMS, or semiconductor devices.
Knowledge of data structuring for reliability tracking and parametric drift analysis.
Experience scaling lab setups toward pilot-line & production line testing frameworks.
Familiarity with Docker, CI/CD for lab code, or deploying test infrastructure at scale.

Enable automation of repetitive validation tasks to reduce test time and improve coverage.

This listing is sourced directly from nEye.ai's careers page and normalized into a canonical job model.