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General Information

Job Title
End of study internship : MLOps
Job ID
13491
Country
France
City
Villeurbanne
Date Posted
21-Nov-2025
Job Category
Interns/Temp
Job Subcategory
Ansys Intern
Hire Type
Intern
Remote Eligible
No

Descriptions & Requirements

Job Description and Requirements
Internship MLOps: migration of an existing ML model prototype to PyTorch (local + cloud)

Introduction: We drive technology innovations that shape the way we live and connect. Catalyzing the era of pervasive intelligence, we deliver design solutions, from electronic design automation to silicon IP, to system design and multiphysics simulation and analysis. We partner closely with our customers across a wide range of industries to maximize their R&D capability and productivity, powering innovation today that ignites the ingenuity of tomorrow.
Internship Experience: At Synopsys, interns dive into real-world projects, gaining hands-on experience while collaborating with our passionate teams worldwide - and having fun in the process! You'll have the freedom to share your ideas, unleash your creativity, and explore your interests. This is your opportunity to bring your solutions to life and work with cutting-edge technology that shapes not only the future of innovation but also your own career path. Join us and start shaping your future today!
Mission Statement: Our mission is to fuel today’s innovations and spark tomorrow’s creativity. Together, we embrace a growth mindset, empower one another, and collaborate to achieve our shared goals. Every day, we live by our values of Integrity, Excellence, Leadership, and Passion, fostering an inclusive culture where everyone can thrive - both at work and beyond.
What You’ll Be Doing:
  • Migrate the prototype of an acoustics anomaly‑detection Machine Learning model from TensorFlow to a PyTorch ecosystem, analyzing technical gaps and trade-offs.

  • Design and implement a reproducible and traceable training pipeline. In a first time for local us (GPU/CPU), and then in the cloud, with experiment tracking (e.g., MLflow).

  • Study and prototype a training pipeline that can be executed on the user side within a constrained local environment.
  • Assess volumetric and infrastructure implications, including scalability, costs, artifact storage, and orchestration.
  • Contribute to the establishment of a complete MLOps framework: instrumentation, packaging, workflow robustness, and industrialization.
What You’ll Need:
  • Currently pursuing a master level degree in Computer Science, Software Engineering, Data Science, or a related field in the penultimate or final year of study.
  • Strong foundations in Python.
  • Knowledge of PyTorch and a solid understanding of training workflows.
  • Familiarity with Docker, cloud platforms (Azure/AWS), experiment tracking, and reproducibility best practices.
  • Experience or interest in MLOps tools (MLflow or equivalent), CI/CD workflows, and artifact management.
  • Rigor, attention to structure and automation, and ability to synthesize, document, and formalize technical decisions.
  • Curiosity, autonomy, and a collaborative mindset within an R&D team.
Key Program Facts:
  • Program Length: 6-month internship
  • Location: Lyon area (Villeurbanne), France
  • Working Model: In office
  • Full-Time/Part-Time: Full-time
  • Start Date: March or April 2026
Equal Opportunity Statement:
Synopsys is committed to creating an inclusive workplace and is an equal opportunity employer. We welcome all qualified applicants to apply, regardless of age, color, family or medical leave, gender identity or expression, marital status, disability, race and ethnicity, religion, sexual orientation, or any other characteristic protected by local laws. If you need assistance or a reasonable accommodation during the application process, please reach out to us.

At Synopsys, we want talented people of every background to feel valued and supported to do their best work. Synopsys considers all applicants for employment without regard to race, color, religion, national origin, gender, sexual orientation, age, military veteran status, or disability.