In this role, we are looking fora strong teamplayer who can help redefine and shape the next generation of software automation and engineering tools through the end-to-end infusion of AI/ML technologies.
 
The ideal candidate will contribute to building intelligent, scalable, and efficient software solutions that accelerate engineering workflows, improve productivity, and support innovation across advanced packaging and silicon development initiatives. This role requires someone who is comfortable working in a dynamic environment, partnering with technical teams, and applying AI/ML across the full software development lifecycle. 

Key Responsibilities 

• Design, develop, and enhance software tools and automation solutions that support engineering workflows in advanced packaging and silicon enablement. 
• Apply AI/ML techniques end-to-end to improve tool capability, automation, decision support, and workflow efficiency. 
• Build and integrate intelligent features into software systems, including data-driven automation, predictive capabilities, and workflow optimization. 
• Collaborate closely with engineering, research, and product teams to understand requirements and translate them into scalable technical solutions. 
• Contribute to the architecture, design, implementation, testing, and deployment of AI/ML-enabled software tools. 
• Develop robust, maintainable, and production-quality code in a fast-paced development environment. 
• Evaluate new AI/ML technologies, frameworks, and approaches to drive innovation in engineering productivity and automation. 
• Support experimentation, prototyping, and iterative refinement of new solutions in a research-focused setting. 
• Ensure software solutions are reliable, scalable, and aligned with engineering and business goals. 
• Participate in code reviews, design discussions, and cross-functional technical problem-solving.

Behavioral Traits 

• Work effectively in a fast-paced, research-driven environment with evolving requirements. 
• Problem-solving, analytical thinking, and communication skills.
• Work independently as well as collaboratively within a multidisciplinary team.​
• Ability to collaborate cross-functionally, contribute to technical design discussions,and deliver high-quality, maintainable software solutions.

The Minimum qualifications arerequiredto be initially considered for this position. Minimum qualifications listed below would be obtained through a combination of industry relevant job experience, internship experience and / or schoolwork/classes/research. The preferred qualifications are in addition to theminimumrequirements and are considered a plus factor in identifyingtop candidates.

Minimum Qualifications:  

• U.S. Citizenship required. 
• Ability to obtain a US Government TS/SCI Security Clearance with Polygraph. 
• Bachelor's degree in computer science, Computer Engineering/Artificial Intelligence, Machine Learning or in a related field. 
• 1+ years of experience designing, deploying, and maintaining scalable, reliable AI/ML production systems.  
• 1+ years of experience with Strong programming skills in one of the following: (C++ (preferred), Python, C# with solid foundations in data structures, algorithms, and software engineering principles). 

Preferred Qualifications 

• Maters in Computer Science, Computer Engineering/Artificial Intelligence, Machine Learning or in a related field.  
• Experience with EDA tools.  
• End-to-end experience with generative AI and LLM-based solutions, including one or more of the following: prompt engineering, embeddings, vector databases, semantic search, and RAG workflows.  
• Hands-on experience building scalable applications, backend services, APIs, or distributed systems, including integration of ML models into software.  
• Experience with machine learning systems, including model training, evaluation, inference, and use of frameworks such as PyTorch, TensorFlow, or Scikit-learn.  
• Experience deploying AI/ML services using Docker, Kubernetes, cloud platforms, REST/gRPC APIs, or serverless architectures.  
• Strong understanding of system design, distributed computing, performance optimization, security, privacy, and responsible AI principles.  
• Experience working with large-scale datasets, data pipelines, or big data/streaming technologies such as Spark, Databricks, or Kafka.  
• Familiarity with databases, version control, testing, CI/CD, and cloud-native development practices.