Internship in AI for Laboratory Automation (Pharma Research & Early Development)
About this opportunity
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
At Roche, we are dedicated to advancing healthcare solutions through innovation and cutting-edge technology. We strive to transform patients' lives and believe that good business contributes to a better world. Our commitment to scientific rigor, ethical standards, and global access to medical innovations drives our daily work towards building a better tomorrow.
Within Roche Pharma Research & Early Development (pRED), the Translational Drug Metabolism, Pharmacokinetics, and Pharmacodynamics (tDMPK/PD) department plays an important role in understanding how potential drug candidates behave in the body. Our work combines experimental research, computational modeling, and advanced analytical technologies to investigate drug properties and support decisions throughout drug discovery and development. We are expanding our capabilities in laboratory automation and digitalization, exploring how new technologies, including artificial intelligence, can improve laboratory workflows and make our research more efficient.
The Opportunity
You will explore how large language models (LLMs) can be used to automate laboratory workflows in pharmaceutical research. The project focuses on connecting LLMs with laboratory equipment and software, allowing users to interact with laboratory systems through natural language and automate selected tasks.
Your work will involve evaluating existing tools and frameworks for LLM-based automation, developing a proof of concept using a laboratory liquid handling system, and assessing the potential and limitations of this approach in a real laboratory environment.
Specifically, you will:
Research and compare existing tools and frameworks for connecting LLMs with external software and laboratory equipment.
Develop a proof of concept that connects an LLM to a laboratory liquid handling system through suitable software interfaces.
Explore how natural language instructions can be translated into executable laboratory workflows.
Design and perform tests to evaluate the reliability, accuracy, and limitations of the developed approach.
Collaborate with laboratory scientists and automation experts to understand existing workflows and identify suitable applications.
Document your implementation, summarize your findings, and provide recommendations for further development.
Who You Are
You hold a Master's degree in Computer Science, Data Science, Engineering, Bioinformatics, or a related field; or are currently enrolled in a relevant Master's or PhD program.
You have strong programming skills in Python and experience with software development.
You have experience with or a strong interest in large language models (LLMs), AI agents, and their integration with external tools.
You are familiar with APIs, software integration, and version control systems such as Git.
You have an interest in laboratory automation and robotics. Previous experience with laboratory instruments or automation software is an advantage.
You enjoy exploring new technologies and solving open-ended technical problems.
You are comfortable working independently and collaborating with colleagues from different scientific backgrounds.
You have a good command of spoken and written English.
Additional Information
The internship (100%) will be located at the Roche Pharma Research Center in Basel.
Please upload your CV and your degree / enrollment certificate online. Include a section in your CV highlighting your relevant skills (2-3 sentences) and provide examples (bullet points) to support them.
Please note that PhD students need to remain enrolled for the entire duration of the internship.
Start: Upon your availability for a period of 6 months with the possibility to extend. We are flexible and encourage you to indicate your ideal starting date in your application.
Please note that due to cantonal regulation reasons, NON-EU/-EFTA nationals need to provide a confirmation by their university stating that an internship is mandatory for their studies.
Ready to take the next step? We'd love to hear from you. Apply now to explore this exciting opportunity!
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.
Job details
How this role compares
Computed from every other active Manufacturing & CMC role in our database, not just this employer's listings.
We currently track 420 comparable Intern/Fellow/Postdoc Manufacturing & CMC roles across 58 biopharma companies.
Salary context
93 of 420 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 420 comparable roles
+ 20 more countries
Seniority mix
420 of 420 peers have a known seniority level
Therapeutic area mix
6 of 420 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
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How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.