Data Science AI/ML Engineer
Advantest, Somerset, NJ, United States
About the Role
Position Overview:
We are seeking highly skilled
Data Science, Machine Learning and AI professionals
to build intelligent systems that automate, optimize and validate PCB design workflows.
The person will work at the intersection of electronics engineering, EDA tools and AI to significantly reduce design cycle time, improve quality and enable next-generation autonomous PCB design capabilities.
The role involves working with large-scale datasets, building predictive, generative models and deploying ML/AI solutions that drive data-driven decision-making and business value.
Data Science
Collect, clean and preprocess structured and unstructured data from multiple sources
Perform exploratory data analysis (EDA) and statistical modeling
Develop dashboards, reports and insights to support business stakeholders
Apply statistical techniques for hypothesis testing and performance measurement
Machine Learning
Design, train and evaluate supervised and unsupervised ML models.
Implement models such as regression, classification, clustering, time series, GNNs, reinforcement learning and optimization algorithms
Apply generative AI & optimize models for accuracy, scalability and performance
Perform feature engineering and model tuning (cross-validation, hyperparameter tuning)
Artificial Intelligence
Develop AI solutions including NLP, computer vision, deep learning, and generative AI
Build and finetune models using frameworks like TensorFlow, PyTorch etc.
Apply LLMs, prompt engineering, RAG pipelines and AI agents where applicable
Ensure AI solutions follow ethical, responsible and explainable AI practices
Deployment & MLOps
• Deploy models into production design workflows
Build CI/CD pipelines for ML workflows
Monitor model performance and handle drift
Collaborate with DevOps and engineering teams
Cross-Functional Collaboration
• Integrate ML solutions with EDA tools
Translate business problems into ML/AI solutions
Communicate findings to technical and non-technical audiences
Collaborate with electrical and manufacturing engineers to align automation with real-world constraints
Qualifications
Required Qualifications
Bachelor's or Master's degree in computer science, Data Science, AI, ML, Statistics or related field
Strong programming skills in Python (required); R or Scala is a plus
Solid understanding of statistics, linear algebra, probability
Hands-on experience with ML algorithms and AI models
Experience working with large datasets and data pipelines
Technical Skills
Core
Python (NumPy, Pandas, Scikit-learn)
Machine Learning algorithms
SQL & NoSQL databases
Data visualization (Matplotlib, Seaborn, Power BI, Tableau)
AI & Deep Learning
Scikit-learn, TensorFlow, PyTorch
Reinforcement Learning
Graph Neural Networks (GNNs)
Computer Vision (OpenCV)
Generative AI and optimization-based AI
Preferred Qualifications
Experience building autonomous or semi-autonomous PCB design systems
Background in manufacturing, testing, or reliability engineering
Exposure to hardware-software co-design
Contributions to EDA tools or academic research in design automation
Experience with electronics manufacturing data
Soft Skills
Strong analytical and problem-solving abilities
Excellent communication and storytelling with data
Ability to work independently and in cross-functional teams
Curiosity and continuous-learning mindset
Position Overview:
We are seeking highly skilled
Data Science, Machine Learning and AI professionals
to build intelligent systems that automate, optimize and validate PCB design workflows.
The person will work at the intersection of electronics engineering, EDA tools and AI to significantly reduce design cycle time, improve quality and enable next-generation autonomous PCB design capabilities.
The role involves working with large-scale datasets, building predictive, generative models and deploying ML/AI solutions that drive data-driven decision-making and business value.
Data Science
Collect, clean and preprocess structured and unstructured data from multiple sources
Perform exploratory data analysis (EDA) and statistical modeling
Develop dashboards, reports and insights to support business stakeholders
Apply statistical techniques for hypothesis testing and performance measurement
Machine Learning
Design, train and evaluate supervised and unsupervised ML models.
Implement models such as regression, classification, clustering, time series, GNNs, reinforcement learning and optimization algorithms
Apply generative AI & optimize models for accuracy, scalability and performance
Perform feature engineering and model tuning (cross-validation, hyperparameter tuning)
Artificial Intelligence
Develop AI solutions including NLP, computer vision, deep learning, and generative AI
Build and finetune models using frameworks like TensorFlow, PyTorch etc.
Apply LLMs, prompt engineering, RAG pipelines and AI agents where applicable
Ensure AI solutions follow ethical, responsible and explainable AI practices
Deployment & MLOps
• Deploy models into production design workflows
Build CI/CD pipelines for ML workflows
Monitor model performance and handle drift
Collaborate with DevOps and engineering teams
Cross-Functional Collaboration
• Integrate ML solutions with EDA tools
Translate business problems into ML/AI solutions
Communicate findings to technical and non-technical audiences
Collaborate with electrical and manufacturing engineers to align automation with real-world constraints
Qualifications
Required Qualifications
Bachelor's or Master's degree in computer science, Data Science, AI, ML, Statistics or related field
Strong programming skills in Python (required); R or Scala is a plus
Solid understanding of statistics, linear algebra, probability
Hands-on experience with ML algorithms and AI models
Experience working with large datasets and data pipelines
Technical Skills
Core
Python (NumPy, Pandas, Scikit-learn)
Machine Learning algorithms
SQL & NoSQL databases
Data visualization (Matplotlib, Seaborn, Power BI, Tableau)
AI & Deep Learning
Scikit-learn, TensorFlow, PyTorch
Reinforcement Learning
Graph Neural Networks (GNNs)
Computer Vision (OpenCV)
Generative AI and optimization-based AI
Preferred Qualifications
Experience building autonomous or semi-autonomous PCB design systems
Background in manufacturing, testing, or reliability engineering
Exposure to hardware-software co-design
Contributions to EDA tools or academic research in design automation
Experience with electronics manufacturing data
Soft Skills
Strong analytical and problem-solving abilities
Excellent communication and storytelling with data
Ability to work independently and in cross-functional teams
Curiosity and continuous-learning mindset