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Helical is on a mission to democratize bio foundation models.
We build the first open-source platform for researchers & computational biologists to explore bio foundation models and create value-added applications on top of those models. We are dedicated to driving innovation in the biotech industry and revolutionize the way pharma and bio companies use AI.
Join us to be part of the early-stage founding team of Helical driving groundbreaking innovation and play a meaningful role in contributing towards us achieving our ambitious goals, while being a part of an inspiring, collaborative and entrepreneurial culture in the heart of Europe.
Our github: https://github.com/helicalAI/helical/ (successful candidates have a good idea of what we are doing here !)
As a Machine Learning Engineer at Helical, you'll collaborate with ML researchers and Computational Biologists to work with bio foundation models and their applications in drug development. Your main role will be to develop, implement and validate machine learning models along with data curation and management approaches. As a member of the founding team, you will be crucial in shaping our platform and build our enterprise business.
· Create your own roadmap and drive forward your own projects
· Collaborate with the core ML team to pioneer computational methods for drug development and other biological applications and deploy large-scale ML-models. · Identify, test and develop new applications and workflows with bio foundation models for various stages of drug research, from target identification to clinical strategies. · Utilize diverse datasets including statistical genetics, genomics, proteomics, etc., to derive biological insights. · Contribute to the ML computational platform, ensuring data accuracy and biological relevance. · Drive team strategy and provide guidance on computational biology to the organization.
Essential:
· MSc (PhD preferably) in Computational Science/Machine Learning with computational biology focus. · Experience with biological data curation and management. · Strong understanding of computational biology tools, methodologies, and best practices. · Ability to communicate scientific concepts effectively. · Programming skills in Python is essential. · Familiarity with data processing pipelines and cloud environments. · Demonstrated career progression and passion for learning. · A passion for AI and Biology!