⌕ Job Overview:
We are looking for a Manufacturing Wafer Level Test Engineer to join our Manufacturing team and play a critical role in developing and operating our wafer-level photonics testing capability. This role will sit at the intersection of photonics, semiconductor testing, automation, and production engineering, helping us transition from development into scalable manufacturing.
You will take ownership of operating, maintaining, and scaling our Wafer Level Test (WLT) capability, including automated optical and opto-electrical measurements across photonic wafers. You will work closely with internal engineering teams and external manufacturing partners to ensure high integrity testing data, robust test protocols, and high throughput execution.
This is a highly hands-on engineering role suited to someone comfortable working across software, hardware, photonics, and semiconductor manufacturing environments. You will be expected to troubleshoot complex systems, improve automation workflows, and help establish repeatable production test methodologies.
✎ What you'll be doing:
Operate, maintain, and optimise wafer-level photonic testing systems
Develop and automate optical and opto-electrical test sequences using Python and LabVIEW
Configure and synchronise metrology hardware including lasers, SMUs, power meters, and fibre alignment systems
Build scalable test workflows capable of supporting high throughput wafer testing
Develop reliable fibre-to-chip alignment procedures with low variance and repeatable measurement quality
Perform optical and opto-electrical characterisation across photonic devices.
Analyse measurement data and troubleshoot hardware or software issues independently
Create and maintain version-controlled test protocols and documentation
Work closely with suppliers and external vendors to transfer internal test recipes into manufacturing environments
Support correlation studies between internal and external testing results
Collaborate with Manufacturing, Photonics, and Reliability teams to support production readiness
𓏗𓏗 What we're looking for:
Industry experience within semiconductor, photonics, or advanced hardware testing environments
Strong Python programming skills for hardware control, automation, or data analysis
Experience using LabVIEW or similar test automation environments
Hands-on experience working with photonic or semiconductor test equipment
Strong understanding of optics, lasers, fibre alignment, and photonic measurements
Experience with automated test systems and metrology hardware
Comfortable working in laboratory environments on a frequent basis
Strong troubleshooting mindset with a practical, hands-on approach to engineering
Ability to communicate clearly with internal stakeholders and external suppliers
✧ Even better if you have:
Experience with wafer-level photonic testing
Familiarity with semiconductor device physics and opto-electrical measurements
Experience analysing diode behaviour and IV curves
Understanding of GDS files and photonic layouts
Experience working with optical fibres and fibre-to-chip alignment systems
Exposure to production or manufacturing test environments
Version control experience using GitHub or similar tooling
Ability and willingness to travel to Asia, USA and EU
We are open to candidates from Physics, Engineering, Mathematics, Data Science, or related technical disciplines.
You will join a deeply technical team building foundational infrastructure for the future of AI systems. This is an opportunity to work on genuinely challenging problems across photonics, semiconductors, and manufacturing, with direct influence on how advanced AI hardware is built and scaled.
You will also have access to cutting-edge equipment, collaborative engineering teams, and the chance to help shape manufacturing capability from the ground up.

Salience Labs is a leader in photonic solutions targeting connectivity for AI data center infrastructure. Salience’s innovative developments in photonic switching technology enable high-speed, ultra-low latency networking fabrics that remove bottlenecks for AI workloads.