Senior Manager, Machine Learning Research

Responsibilities

• Develop and implement the strategic vision for the ML Research group, ensuring alignment with company goals.
• Serve as a thought leader in state-of-the art ML research, using the latest approaches in diffusion models, equivariant neural nets and predictive ML  Represent Genesis Therapeutics at conferences, workshops, and scientific forums.
• Guide the team in designing, implementing, and validating novel machine learning models tailored to drug discovery challenges, including molecular property prediction, generative modeling, and protein-ligand interactions.
• Prioritize and balance short-term project deliverables with long-term foundational research initiatives.
• Ensure high standards of scientific rigor and reproducibility in research outputs.
• Pioneer cutting edge research and original publications in top industry journals and conferences, including NeurIPS, ICML and more.
• Partner with cross-disciplinary teams, including computational chemists, software engineers, and drug discovery scientists, to deliver impactful ML-driven solutions.
• Collaborate with the Engineering team to deploy ML models into scalable production environments.
• Communicate findings and progress effectively to stakeholders at all levels: from interns to board of directors.
• Manage budgets, resource allocation, and project timelines for the ML Research group.
• Identify and establish external collaborations and partnerships to augment in-house capabilities.
• Lead and mentor a high-performing team of ML Research Scientists and ML Research Engineers.
• Foster a collaborative, innovative, and growth-oriented team culture.
• Manage recruitment, performance evaluations, and career development for team members.

Qualifications

• PhD in Computer Science, Machine Learning, Physics, or a related field.
• 4+ years of professional experience in machine learning research, with at least 1+ years in a tech or people leadership role.
• Strong track record of developing and applying ML methods in areas such as deep learning, generative models, graph neural networks, or equivariant neural networks.
• Proven expertise in leveraging ML techniques for real-world problems. It is not a requirement that our ML leaders have deep experience in the drug discovery domain but must have demonstrated ability to champion great research and intellectual curiosity to learn quickly.
• Experience with state-of-the-art ML frameworks (e.g., PyTorch, TensorFlow, JAX) and distributed computing environments.
• Demonstrated ability to lead, mentor, and inspire a diverse team of researchers and engineers.
• Exceptional communication and collaboration skills, with experience bridging the gap between research and application.
• Strategic thinking with a balance of creativity and execution focus.