Senior Data Scientist, Software Engineering

Kulicke & Soffa•Fort Washington, PA

16h•$109,845 - $150,000•Hybrid

About The Position

This role involves researching, designing, and implementing advanced machine learning (ML) solutions, including image classification, time series, and waveform-based models, specifically for semiconductor manufacturing and related applications. The position requires developing and optimizing embedded software components to integrate ML algorithms into proprietary hardware systems, ensuring real-time performance and reliability. A key aspect is architecting and implementing end-to-end MLOps pipelines, covering data ingestion, preprocessing, model training, deployment, monitoring, and lifecycle management, utilizing cloud platforms and container orchestration technologies. The role also includes integrating labeling systems into the MLOps lifecycle, designing and operating reliable data stores for MLOps, and building/maintaining data pipelines for large-scale data processing and feature engineering. Additionally, the position involves designing and developing web-based applications for data visualization and control of ML-driven solutions, creating automated processes for data cleansing and anomaly detection, and collaborating with cross-functional teams to translate requirements into AI-driven solutions. The Senior Data Scientist will also develop software modules and visualization tools for interpreting machine and process signals, implement CI/CD workflows for ML applications, and communicate technical findings to stakeholders, providing technical leadership and serving as an internal expert.

Requirements

Experience designing and implementing ML-based solutions, including both image classification models and waveform-based ML models.
Experience with Python for data science and ML development.
Experience with embedded software programming with C++ and OOP, web application development, and databases.
Experience integrating ML components into production environments, optimize performance, and ensure scalability across distributed systems.
Experience with data preprocessing, feature engineering, and workflow automation for ML models.
Experience with containerization (e.g., Docker, Podman) and orchestration tools (e.g., Kubernetes).
Experience designing and implementing full-stack AI solutions, from embedded systems to cloud-based services, ensuring robust security and compliance.
Experience with Git, CI/CD pipelines, and automated deployment strategies for ML applications.
Bachelor’s degree or foreign equivalent in Data Science, Computer Science or a closely related field.
At least two (2) years of experience in a Data Scientist, Software Engineer or related occupation.

Responsibilities

Research, design, and implement advanced machine learning (ML) solutions, including image classification, time series and waveform-based models, for semiconductor manufacturing and related applications.
Develop and optimize embedded software components that integrate ML algorithms into proprietary hardware systems, ensuring real-time performance and reliability.
Architect and implement end-to-end MLOps pipelines, including data ingestion, preprocessing, model training, deployment, monitoring, and lifecycle management, leveraging cloud platforms and container orchestration technologies.
Integrate labeling systems into the MLOps lifecycle (e.g., human-in-the-loop labeling, active learning, dataset curation tools) and design, implement, and operate reliable data stores for MLOps, including object storage, time-series databases, feature stores, and metadata registries, with robust data lineage, provenance tracking, governance, access control, and auditability.
Build and maintain data pipelines for large-scale data processing, feature engineering, and model development, ensuring robustness and scalability across distributed environments.
Design and develop web-based applications and services to deliver data visualization, configuration, and operational control of ML-driven solutions, integrating with backend servers and cloud infrastructure.
Create and maintain automated processes and algorithms for data cleansing, anomaly detection, and interpretation of complex signals from manufacturing hardware.
Collaborate with cross-functional teams to translate business and engineering requirements into actionable AI-driven solutions, including defining experiments, validation plans, and performance metrics.
Develop software modules and visualization tools for interpreting machine and process signals, enabling actionable insights for R&D and production optimization.
Implement CI/CD workflows for ML applications, ensuring seamless integration, version control, and automated deployment across environments.
Communicate technical findings and analytics insights to stakeholders, provide technical leadership and guidance to cross-functional teams, and serve as an internal expert on ML, MLOps, and software integration.