
Machine Learning Engineer
- Irvine, CA
- $112,500-215,900 per year
- Permanent
- Full-time
- Predictive analytics for yield and quality improvement
- Optimization of circuit or device parameters
- RF signal modeling and anomaly detection
- Root-cause analysis across test and manufacturing data
- Own the end-to-end ML pipeline: data acquisition, feature engineering, model selection, training, evaluation, deployment, and monitoring.
- Collaborate with cross-functional stakeholders including design engineers, process experts, and product owners to understand problem statements and translate them into ML solutions.
- Work on both research-oriented prototypes and production-grade deployments.
- Stay up-to-date with current trends in ML, especially those relevant to semiconductor, signal processing, or high-dimensional time-series data.
- BS and 8 years experience (Ph.D. preferred) in Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a related field.
- Strong theoretical and practical experience in machine learning and deep learning, including CNNs, transformers, time-series models, or probabilistic methods.
- Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, Scikit-learn.
- Solid understanding of statistics, optimization, and model evaluation techniques.
- Excellent communication and collaboration skills, with the ability to work across disciplines and teams.
- Exposure to semiconductor, electronics, or RF system domains (e.g., basic understanding of circuit behavior, test data, signal integrity).
- Experience working with large, complex datasets (e.g., sensor, waveform, or EDA simulation outputs).
- Familiarity with data infrastructure and workflow orchestration (e.g., Airflow, MLflow, or cloud platforms).
- Contributions to peer-reviewed research, patents, or open-source ML projects.