Software Engineer - Machine Learning for EDA Systems

General Summary:

Software Engineer — Machine Learning for EDA Systems

Minimum Experience: 2+ years in software development 

🌟 Role Overview

We are looking for a seasoned Software developer with 2-5 years experience in designing, developing, deploying, and scaling software platforms in the realm of Electronic Design Automation (EDA). You will work in cutting-edge initiatives that fuse Machine Learning with semiconductor design, driving robust systems that transform chip development workflows.

Minimum Qualifications:

🏗️ Key Responsibilities

Design and develop end-to-end ML-integrated software platforms built for scale, performance, and adaptability.

Strong software development skills with experience in training and productizing Machine learning models, fostering innovation and engineering excellence.

Good software design skills, knowledge of software development life cycle, embedding best development practices and agility across teams.

Help in advanced software algorithm design tailored for automation and optimization in EDA flows.

Engineer reliable and scalable data pipelines to fuel ML model development and inference.

Knowledge of full-stack deployment strategies, enabling ML applications to function across diverse environments.

Establish and maintain comprehensive ML Ops infrastructure, supporting continuous training, deployment, and monitoring.

Experience with CI/CD pipelines, quality assurance standards, and system scalability from prototype to production.

Ensure reliability and scalability of large system deployments for high-performance compute platforms.

💡 Required Skills & Experience

At least 2+ years of experience in software development, with a focus on building and maintaining scalable machine learning projects.

Strong coding chops in one or more programming languages - Python , C++, Typescript and frameworks like TensorFlow, PyTorch, or Scikit-learn with knowledge of software development tools and best practices.

Solid grasp of CI/CD tools (e.g. GitLab, Jenkins) and working knowledge on MLOps platforms (e.g., MLflow, Kubeflow, Airflow)

Handson in various databases and skilled in data engineering and cloud/on-prem infrastructures (AWS, GCP, Kubernetes).

Sharp communication skills with a track record of cross-functional collaboration.

🏆 Preferred Qualifications

MS or PhD in Computer Science, Electrical Engineering, or a related field.

Strong software coding and software design skills in Python / C++ .  Should be aware of best software development practices, frameworks and tools.

Knowledge in advanced ML techniques—e.g., graph neural networks or reinforcement learning, Generative AI is preferred.

Contributions to open-source, patents, or technical publications related to ML-driven design automation.

Knowledge of EDA tools and flows is preferrable, including experience with platforms such as Synopsys, Cadence, or Siemens.

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