Software Engineer

Intel Intel · Semiconductors · Arizona, Phoenix, United States +1

Software Engineer at Intel's Advanced Packaging and Technology Manufacturing Group, focusing on applying AI/ML to develop and enhance software automation and engineering tools for semiconductor development. The role involves building intelligent features, integrating AI/ML into systems, and collaborating with engineering teams to accelerate workflows and improve productivity. Requires experience with software development, AI/ML application, and programming languages like Python.

What you'd actually do

  1. Design, develop, and enhance software tools and automation solutions that support engineering workflows in advanced packaging and silicon enablement.
  2. Apply AI/ML techniques end-to-end to improve tool capability, automation, decision support, and workflow efficiency.
  3. Build and integrate intelligent features into software systems, including data-driven automation, predictive capabilities, and workflow optimization.
  4. Collaborate closely with engineering, research, and product teams to understand requirements and translate them into scalable technical solutions.
  5. Contribute to the architecture, design, implementation, testing, and deployment of AI/ML-enabled software tools.

Skills

Required

  • Master’s degree in AI/ML, Data Science/ Computer science, Software Engineering, Electrical Engineering.
  • 6 months of experience in software development skills with experience in building engineering applications, automation tools, or enterprise software solutions.
  • Hands-on experience with AI/ML technologies and their practical application in software systems.
  • Programming languages such as Python, C++, Java, or similar.
  • Software design, development, testing, and debugging in a collaborative environment.

Nice to have

  • Hands on experience designing, deploying, and maintaining scalable, reliable AI/ML production systems.
  • End-to-end experience with generative AI and LLM-based solutions, including prompt engineering, embeddings, vector databases, semantic search, and RAG workflows.
  • Foundations in data structures, algorithms, and software engineering principles.
  • 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.
  • Familiarity with MLOps/LLMOps practices, including model versioning, experiment tracking, monitoring, and performance evaluation.
  • 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 (SQL/NoSQL), version control, testing, CI/CD, and cloud-native development practices.

What the JD emphasized

  • end-to-end infusion of AI/ML technologies
  • end-to-end experience with generative AI and LLM-based solutions

Other signals

  • applying AI/ML across the full software development lifecycle
  • building and integrating intelligent features into software systems
  • end-to-end experience with generative AI and LLM-based solutions