• Thoughtful: Emphasizes curiosity, creativity, and care in rigorous scientific work.
• Brave: Encourages fearlessness, initiative, and integrity in tackling challenges.
• Determined: Focuses on confidence in hypotheses and the urgency needed to deliver results.
• Together: Promotes connection and collaboration across fields to achieve impactful transformation.

IsoLabs is committed to creating an extraordinary company culture that values diverse skills, backgrounds, and perspectives, fostering an environment where collaboration and support are fundamental.

About the Role

Job Title: Research Scientist (Machine Learning)
Location: Lausanne
Employment Type: Full-time
Department: Research

Your Impact

As a Research Scientist in machine learning, you will:

• Build machine learning models and algorithms to transform drug discovery.
• Collaborate with engineers and scientists to develop cutting-edge ML algorithms.
• Lead projects and provide mentorship within the ML community.

Responsibilities

• Contribute to research directions in machine learning for drug discovery.
• Identify and create novel ML techniques and data for training.
• Develop architectures and training algorithms for ML models.
• Analyze and tune experimental results to inform future directions.
• Implement and scale training and inference engineering frameworks.
• Present research findings clearly to diverse audiences.
• Collaborate with domain experts and suggest team collaborations.

Skills and Qualifications

Essential:

• PhD or equivalent practical experience in a technical field.
• Proven track record in machine learning using deep learning techniques.
• Strong knowledge of linear algebra, calculus, and statistics.
• Experience with ML frameworks (e.g., JAX, PyTorch, TensorFlow) and scientific software (e.g., NumPy, SciPy, Pandas).
• Passion for applying ML research to real-world problems.

Nice to Have:

• PhD in machine learning or computer science.
• Relevant research experience (e.g., postdoctoral roles, publications).
• Experience in scientific environments across disciplines (biology, chemistry, physics).
• Familiarity with biological or chemical data and software.
• Experience with real-world datasets and ML on accelerators.
• Knowledge in large-scale deep learning, generative models, and applied reinforcement learning.

Hybrid Working

IsoLabs follows a hybrid working model, requiring in-office attendance three days a week (currently Tuesday, Wednesday, and one additional day). Accommodations for additional needs can be discussed during the initial screening call.