Senior Engineer - AI/ML

Job Description Primary Duties & Responsibilities

• Design, develop, validate, and deploy machine-learning solutions that improve optical transceiver calibration quality, test time, yield, and equipment utilization.
• Build supervised-learning models for prediction, classification, regression, anomaly detection, and early test-outcome prediction using high-volume production and engineering test data.
• Evaluate and apply optimization techniques—including Bayesian optimization, reinforcement learning, and hybrid/model-based approaches—where they provide measurable benefit for multi-parameter tuning and expensive test operations.
• Develop robust feature-engineering, data-quality, model-training, and model-evaluation pipelines for test and trace data.
• Integrate ML inference and optimization services with production test software, Python APIs, databases, and existing automation frameworks.
• Establish model validation criteria, including accuracy, repeatability, test-time reduction, false-pass/false-fail risk, yield impact, and fallback behavior.
• Monitor production model performance, data drift, and operational failures; implement versioning, logging, alerting, rollback, and retraining mechanisms.
• Analyze test and manufacturing data to identify bottlenecks, correlations, failure signatures, and opportunities for test-suite optimization.
• Work closely with test-development, product engineering, manufacturing, and software teams to translate technical requirements into deployable solutions.
• Mentor junior engineers through technical reviews, design guidance, and hands-on problem solving.
• Prepare clear technical documentation, results summaries, and business-impact reporting for engineering and management stakeholders.
• Contribute to invention disclosures, patents, or technical publications where applicable.

Education & Experience

• Bachelor’s or master’s degree in Computer Science, Electrical/Electronics Engineering, Applied Physics, Data Science, or a related discipline.
• 3–5 years of hands-on experience developing and deploying applied AI/ML solutions in an engineering, manufacturing, test, semiconductor, optics, industrial, or similar environment.
• Demonstrated experience delivering at least one ML solution beyond proof of concept, including integration, validation, and operational support.
• Experience working with time-series, sensor, measurement, manufacturing, test, or high-dimensional engineering data is strongly preferred.
• Experience in optical transceivers, semiconductor test, high-speed electronics, signal integrity, or hardware test automation is a strong advantage.

Required Technical Skills

• Strong Python software-development skills, including clean, maintainable, testable, and production-oriented code.
• Hands-on experience with PyTorch, scikit-learn, XGBoost/LightGBM, or equivalent ML frameworks.
• Strong understanding of model development and evaluation: data preparation, feature engineering, cross-validation, metrics, bias/variance, model explainability, and error analysis.
• Experience with regression, classification, anomaly detection, and statistical analysis of engineering or manufacturing data.
• Experience building or consuming REST APIs and integrating ML components into larger software systems.
• Working knowledge of SQL and data-processing tools such as pandas, NumPy, and Parquet/CSV-based data pipelines.
• Familiarity with source control, code review, CI/CD, containerization, and Linux-based development environments.
• Strong problem-solving, communication, and cross-functional collaboration skills.

Preferred Skills

• Experience with Bayesian optimization, reinforcement learning, multi-armed bandits, or constrained optimization applied to real-world systems.
• Experience with MLOps practices such as experiment tracking, model registry, model versioning, monitoring, drift detection, and retraining workflows.
• Experience with FastAPI, Docker, PostgreSQL, Elasticsearch, Airflow, MLflow, or similar production data and ML platforms.
• Familiarity with test automation or instrumentation interfaces, including oscilloscopes, DCA, BERT, optical power meters, source-measure units, or equivalent equipment.
• Knowledge of optical-transceiver test parameters such as extinction ratio, OMA, BER, TDECQ, equalization, bias control, and calibration workflows.
• Experience with statistical process control, yield analysis, correlation studies, and measurement-system an…