As an Engineer I, you will play a key role in identifying sources of yield loss within semiconductor fabrication processes. Your work will center on distinguishing between systematic and random defects, then guiding engineering teams to implement corrective and preventive actions that enhance manufacturing performance.
Key Responsibilities
- Analyze yield data using statistical methods and machine learning to detect patterns and root causes across process steps
- Apply knowledge of semiconductor physics, device behavior, and circuit design to interpret electrical test results and process variations
- Investigate tool parameters and wafer-level test data to isolate impacted materials and timeframes
- Assess process changes by evaluating statistical significance in test outcomes to guide risk-informed decisions
- Develop predictive models for defect behavior and communicate findings to relevant teams for resolution
- Integrate critical process variables into baseline yield models to improve forecast accuracy and set realistic production targets
- Use coding in Python and R to automate data analysis, visualization, and reporting workflows
Qualifications
Candidates must hold a Master’s degree in Computer Science, Data Science, Computer Engineering, or a closely related field. Demonstrated experience through academic or professional work is required in the following areas:
- Proficiency in Python for data analysis using libraries such as Numpy, Pandas, Scikit-learn, Matplotlib, and Plotly
- Experience with big data platforms including Spark, Hadoop, and PostgreSQL
- Strong foundation in statistical methods: hypothesis testing, Bayesian inference, stochastic modeling, bootstrapping, and correlation analysis
- Familiarity with deep learning frameworks like PyTorch and TensorFlow, particularly in image processing and NLP applications
- Knowledge of classical machine learning techniques including regression, boosting, and bagging algorithms
- Ability to create clear 2D and 3D visualizations for technical reporting
- Experience in Design of Experiments (DOE) and change impact assessment
- Proficiency in R for data manipulation, analysis, and reporting
Work Environment
This is a full-time, on-site role located in Austin, Texas. All candidates must be able to work on-site without reliance on remote accommodations.
Compensation & Benefits
The salary range for this position is $107,515 to $127,515. Additional compensation may include participation in company incentive programs based on performance. Benefits include medical, dental, and vision coverage; life insurance; 401(k) with immediate vesting; paid parental leave; paid time off; personal and regular holidays; and wellness incentives. Onsite amenities include cafés and fitness facilities.
