THE ROLE:
We are looking for a practical and experienced Staff Product Development Engineer (Data and AI Scientist) with a strong background in end-to-end development and deployment of AI and machine learning solutions for the semiconductor industry.
THE PERSON:
The ideal candidate should be comfortable working with large-scale datasets and designing algorithmic and scalable solutions. The candidate should also have excellent interpersonal skills for collaborating with engineering and business teams to deliver impactful outcomes.
KEY RESPONSIBILITIES:
- Design, develop, and deploy end-to-end machine learning pipelines-from data ingestion to model serving
- Work with large, complex data sets to extract insights and build predictive or decision-support models
- Implement scalable solutions that integrate with enterprise data and software systems
- Ensure reliability, maintainability, and performance of deployed models in production environments
- Collaborate with cross-functional teams to translate real-world challenges into effective data-driven solutions
- Contribute to internal tooling and frameworks that accelerate AI/ML delivery
PREFERRED EXPERIENCE:
- Background in the semiconductor or electronics manufacturing domain
- 7+ years of hands-on experience in deploying machine learning models and systems in production
- Strong programming skills in Python, with experience in machine learning and/or deep learning libraries such as pandas, numpy, scikit-learn, PyTorch, or TensorFlow
- Proven experience working with large-scale data processing, including distributed data environments (e.g., SQL, Spark, or cloud-native solutions)
- Solid understanding of software engineering practices-version control, CI/CD, unit testing, containerization, and system monitoring, by using tools like GitHub, Bitbucket, etc.
- Contributions to open-source projects or public AI/ML work (e.g., GitHub, blogs, academic publications)
- Experience with platforms or tools like Snowflake, Databricks, MLflow, Airflow, graph database, LLM and agentic AI framework (e.g., LangChain, etc.).
- Familiarity with one or both of the following:
- Large Language Models (LLMs)-based agentic workflow implementations
- Graph-based analytics or knowledge-driven modeling
ACADEMIC CREDENTIALS:
- Master's in Computer Engineering, Computer Science, Data Science, or a related technical field
