Head Of Ai Structural Biology Research
2 months ago
Key Responsibilities:
- Lead and oversee a team of 4-6 AI/ML researchers, providing technical direction and promoting a culture of creativity and innovation.
- Drive the development and implementation of cutting-edge AI/ML models and algorithms, with a focus on amino acid sequence generation and 3D protein structure prediction.
- Collaborate with cross-functional teams of lab scientists, program managers, and software engineers to set research goals and translate discoveries into real-world applications.
- Manage and prioritize multiple research initiatives, ensuring they align with organizational objectives and timelines.
- Contribute to the design and architecture of AI systems, balancing research exploration with the needs of production-level implementations.
- Serve as a thought leader within the team, particularly in areas related to antibody engineering methodologies.
- Mentor and support the growth of team members as AI researchers and engineers.
- Represent the organization’s AI research capabilities at conferences, in scientific publications, and through collaborations with academic and industry partners.
- Uphold and ensure adherence to the company’s core values across all projects and interactions.
Qualifications:
- Ph.D. in Computer Science, Machine Learning, Computational Biology, or a related field.
- Over 5 years of experience in AI/ML research, with at least 2 years in a leadership or technical lead capacity.
- Strong track record of publications or demonstrated expertise in areas such as generative AI, deep learning, or computational biology.
- Experience applying AI methodologies to biological challenges, particularly in protein design or drug discovery.
- Proven experience leading AI research projects and successfully transitioning research into practical solutions.
- Excellent communication skills, capable of explaining complex AI concepts to various audiences.
- Strong analytical and problem-solving abilities, with a critical mindset towards AI research challenges.
Preferred Qualifications:
- Experience with large-scale multi-node training on HPC GPU clusters or in cloud environments like AWS.
- Familiarity with NGS, SPR, or Flow Cytometry data analysis.
- Previous experience managing AI research teams.
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