Principal Machine Learning Scientist

About Bayer

At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where 'Health for all, Hunger for none' is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives, expanding our thinking, growing our capabilities and redefining ‘impossible’.

About the Role

The Principal Machine Learning Scientist will develop novel machine learning algorithms and workflows for accelerating early-stage drug discovery. In this role, you are responsible for constructing, studying, and training algorithms that learn from complex, high-dimensional data to uncover patterns and develop practical predictive models and applications. This involves utilizing various techniques, such as random forests, deep learning, and neural networks, to enhance predictive capabilities, particularly in natural language processing and machine perception. The focus is on simulating human learning activities, improving system performance through data analysis, and developing deep learning frameworks and systems that operate independently of explicit programming instructions.

Responsibilities

• Develop, evaluate, and apply machine learning algorithms and workflows for accelerating early-stage drug discovery, including but not limited to:
  • De-novo design of biomolecules
  • Assessment of target druggability across therapeutic modalities
  • Design of drug delivery systems
  • Identification of novel druggable pockets and epitopes
  • Characterization of protein-protein and protein-ligand interactions
• Contribute to the implementation, validation, and improvement of machine learning tools and software solutions that support drug discovery activities
• Identify opportunities for accelerating ongoing drug discovery projects with internal and external AI capabilities
• Communicate, educate, and engage with a broad set of stakeholders (chemists, biologists, computational/data scientists, R&D leadership) on the state of technology and the progress of key internal initiatives
• Engage with the broader scientific community through publications, talks, and open-source contributions
• Keep up to date with the latest advances in AI-driven modeling of biomolecular structure and dynamics

Requirements

• Ph.D. degree in Computational Chemistry/Biology, Chem/Bioinformatics, Chemical/Biological/Molecular Engineering, or a related field at the intersection of life sciences and computer science
• Deep expertise with state-of-the-art machine learning methods for modeling biomolecules, like co-folding and/or generative methods for protein design
• Expertise in handling, processing, integrating, and analyzing large datasets related to drug development research, including biochemical, biophysical, and structural biology data
• Strong programming skills in Python
• Demonstrated commitment to scientific rigor, a track record of scientific excellence, strong analytical thinking, and a high degree of self-motivation
• Excellent written and verbal communication skills

Preferred Qualifications

• 5+ years of relevant post-PhD experience, including 2+ years in industry
• Experience with established, physics-based protein modeling methods like Molecular Dynamics and/or Rosetta
• Experience in coordinating small, interdisciplinary teams and ability to articulate their impact to managerial stakeholders
• Strong record of publications or patents related to machine learning solutions for biomolecular modeling

Pay

Employees can expect to be paid a salary between $128,000.00 - $192,000.00. Additional compensation may include a bonus or commission.

Benefits

• Healthcare
• Vision
• Dental
• Retirement plans
• Paid time off (PTO)
• Sick leave

This salary range is merely an estimate and may vary based on an applicant’s location, market data/ranges, skills, prior relevant experience, certain degrees and certifications, and other relevant factors.