Level

Roche

Utilization of Physical AI for Fast and Efficient Implementation of Robotic-Assisted Processes in Diagnostic and Pharmaceutical Raw Material Production (m/f/d)

Roche is hiring a Utilization of Physical AI for Fast and Efficient Implementation of Robotic-Assisted Processes in Diagnostic and Pharmaceutical Raw Material Production (m/f/d). Level rates it ; you can apply on Level.

AI in this role

pytorchtensorflowjax

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections,  where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

The Position

About the project:


The implementation of robotics and automation in industrial environments currently requires high investment, significant engineering effort, and extensive setup times. Consequently, automation is often only economically viable for processes characterized by high repetition, low uniformity, low variance, or strict requirements for product and personal protection.


This PhD project aims to drastically reduce the implementation effort and associated costs of robotic systems. By lowering these barriers, automation can become viable for flexible, low-volume, and high-variance production environments. The core focus of this research is the utilization of Physical AI for fast, intuitive, and efficient training and adaptation of robots. The project will evaluate key factors required to achieve effortless implementation while simultaneously ensuring high process robustness and safety.


Degree: Master’s degree (or equivalent) in Robotics, Computer Science, Mechanical/Electrical Engineering, Automation Engineering, Physics, or a closely related field.


Your tasks and responsibilities:


Physical AI & Machine Learning:

  • Solid understanding of Machine Learning (ML) and Deep Learning (DL) architectures
  • Familiarity with concepts like Reinforcement Learning (RL), Imitation Learning (Learning from Demonstration), or Physics-Informed Neural Networks (PINNs)
  • Experience applying AI to physical systems or hardware

Robotics & Automation:

  • Strong foundation in robot kinematics, dynamics, and control
  • Experience with robotic frameworks, primarily ROS / ROS2 (Robot Operating System)

Software Engineering:

  • Advanced programming skills in Python and/or C++.Experience with ML frameworks such as PyTorch, TensorFlow, or JAX.Familiarity with simulation environments (e.g., Isaac Sim, MuJoCo, Gazebo)
  • Domain Knowledge & Soft Skills

Industrial Application: Basic understanding of production environments, ideally regarding process safety, robustness, or flexible manufacturing (low-volume, high-variance)

Regulated Environments: Interest in or prior exposure to strict production standards (like cleanrooms or pharmaceutical/diagnostic constraints) is a strong plus

Analytical Mindset: Ability to bridge abstract AI theory with practical, real-world robotic deployment

Communication & Collaboration: Strong team player capable of working in interdisciplinary teams and presenting research at international conferences


Your Advantages: 

  • 3,750 € salary per month for a full-time
  • The possibility of accommodation in the Roche Boarding House or convenient transport connections to Munich with the Roche Shuttle

 

Your Application:

Please upload your Curriculum vitae specific to this position.

 

Start: As of January 2027 or according to the agreement for a period of 3 years.

 

We are excited to see your application!



 

 

Your contact to us! With people. For people.

Do you need further support?

Please have a look at our FAQs, you will find them via https://careers.roche.com/global/en/faq.

Who we are

A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.


Let’s build a healthier future, together.

Roche is an Equal Opportunity Employer.

How we rate this

Utilization of Physical AI for Fast and Efficient Implementation of Robotic-Assisted Processes in Diagnostic and Pharmaceutical Raw Material Production (m/f/d) at Roche rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

PyTorchTensorFlowJax

Questions you could be asked

  1. What's a project where you used PyTorch hands-on?
  2. Walk me through how you've used TensorFlow in your day-to-day work.
  3. What are the limits of Jax that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: PyTorch, TensorFlow, and Jax. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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