Primary Duties & Responsibilities

• Assist in developing AI/ML models to analyze test data from optical transceiver, 3D sensing, or high-speed hardware test systems.
• Support supervised ML model development using algorithms such as Random Forest, XGBoost, LightGBM, or neural networks to predict test outcomes from early measurements.
• Explore anomaly detection techniques such as Isolation Forests, clustering, or autoencoders to identify unusual test behavior or quality risks.
• Help evaluate optimization techniques such as Bayesian Optimization, reinforcement learning, or multi-armed bandits for reducing tuning and test cycle time.
• Work with senior engineers to analyze multi-parameter tuning problems involving bias currents, LUTs, equalizer settings, or calibration parameters.
• Develop Python scripts and tools for test data cleaning, feature extraction, visualization, and model evaluation.
• Assist in integrating ML prototypes with existing test automation frameworks, Python APIs, or lab test environments.
• Analyze test station data to identify opportunities for test pruning, early termination, yield improvement, and throughput optimization.
• Create reports, dashboards, or notebooks summarizing model performance, test-time savings, and key engineering insights.
• Collaborate with AI/ML engineers, test engineers, hardware engineers, and manufacturing teams to understand test flows and data requirements.
• Document experiments, model assumptions, results, and recommendations clearly for technical review.

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Data Science, Applied Physics, Machine Learning, or a related field.
• Coursework, academic projects, internships, or research experience in machine learning, data analysis, statistics, signal processing, or test automation.
• Experience working with Python for data analysis, scripting, or ML model development.
• Exposure to hardware testing, optical communications, electronics, sensors, or manufacturing test environments is a plus.
• Prior experience with lab instruments, automated testing, or large-scale test datasets is preferred but not required.

Skills

- Strong Python development skills; proficiency with ML libraries (PyTorch, TensorFlow, scikit-learn, RLlib).
- Hands-on with automation frameworks and hardware interfacing (DCAs, BERTs, oscilloscopes).
- Knowledge of ML algorithms: supervised learning, reinforcement learning, Bayesian methods, anomaly detection.
- Domain expertise in optical transceiver testing (TDECQ, ER, OMA, LUT calibration, BER tests) is a plus
- Strong analytical, leadership, and cross-functional collaboration skills.
- Experience working with statistical analysis tools for test data (SPC, yield analysis, correlation studies).

• Assist in developing and optimizing AI/ML models for optical transceiver testing, ensuring accuracy and efficiency.
• Integrate AI into calibration and validation processes, working closely with the engineering team.
• Collect, preprocess, and analyze data for model training and improvement, a crucial step in our AI journey.
• Support the team in identifying and solving complex engineering problems using AI/ML techniques, a key challenge in our industry.
• Design and implement automated testing systems with AI integration, revolutionizing our testing processes.
• Stay updated on the latest AI and ML advancements for optical communications, a critical aspect of our work.
• Document and present your work, providing insights and recommendations to the team and stakeholders.
• Engage in knowledge sharing, contributing to a collaborative and innovative work culture.
• Assist in preparing technical reports and presentations, communicating our progress and findings.

• Pursuing a Bachelor's or Master's in Computer Science, Electrical Engineering, or a related field with an AI/ML focus.
• Strong programming skills in Python, C++, or Java, a must-have for our AI endeavors.
• Knowledge of machine learning algorithms and their applications, especially in optical communications, is essential.
• Experience with data analysis and processing tools is preferred, a key asset for our data-driven approach.
• Familiarity with optical transceivers and testing processes is advantageous, providing a solid foundation.
• Excellent problem-solving and analytical skills, crucial for tackling complex engineering challenges.
• Ability to work independently and collaboratively, a key trait for our team environment.
• Good communication skills for effective knowledge sharing and collaboration.
• Passion for innovation and a desire to contribute to cutting-edge technology, a driving force for our team.
• Willingness to learn and adapt, embracing new technologies and methodologies.