Senior AI Computational Biologist

About the Role

SandboxAQ’s AI Simulation team develops new drugs and materials using a spectrum of AI and physics-based computational solutions. We are seeking an experienced and innovative Senior AI Computational Biologist to amplify our ability to reason causally about biological systems, based on multimodal data from a variety of inputs, including simulation. The successful candidate will have significant expertise in computational biology, including knowledge of cutting-edge machine learning techniques, particularly involving large graph data structures such as knowledge graphs. They will have experience leading small teams in the delivery of innovative solutions to customers. These skills will be leveraged within a seasoned, agile, and multi-disciplinary group, including drug hunters with an excellent track record in drug discovery, computational chemists, physicists, AI experts, and software engineers.

What You'll Do

• Lead projects combining ML/AI solutions, knowledge graphs, bioinformatics, biophysical simulations, and software development to optimize drug discovery and development strategies.
• You will guide a technical team to innovate in areas including disease biology, bioinformatics, cheminformatics, AI (e.g., ML, NLP, etc.) using your blend of expertise and knowledge in these areas.
• Apply your experience in both technical leadership and people management to build a successful and inclusive team.
• Your work will directly impact decision-making in drug discovery and development projects.
• Partner with researchers from diverse backgrounds to develop novel computational methods to infer causality in disease progression.
• Provide broad insight and lead drug projects by providing support on studies in multiple scientific areas including target identification and validation, biomarker discovery, patient stratification, clinical trial design, etc.
• Interpret experimental data from biological studies in immunology, oncology, and neurodegeneration.
• Analyze omics and phenotyping data from research studies, clinical trials, and real-world databases to identify disease-driving cell types, pathways, and genes
• Propose drug targets based on the biology insights derived from data and suggest validation experiments

About You

• You’re someone who wants to drive your own development. 
• You’re looking for a company where you have the opportunity to pursue your interests across functions and geographies. 
• You are inspired by our mission and have a collaborative, rigorous, and entrepreneurial spirit that matches SandboxAQ’s global culture.
• PhD in computational biology or similar field with strong innovation and publication record.
• 10+ years of relevant professional experience, including pharma/biotech and postdoctoral research.
• Experience mentoring direct and/or indirect reports.
• Excellent analytical capability, being able to identify key questions and come up with innovative solutions for complex problems.
• You have a unique blend of passion for biology, strong bioinformatics experience (omics, pathway, network analysis, etc.), AI, software development, and literacy on experimental biology to be an effective leader of your team.
• Enjoys the startup work culture with a great collaborative spirit.
• A strong team player with good communication skills (especially with experimental biologists).

Nice to Haves

• Proven background in physics-based and AI-based molecular simulation.
• Experience in target identification.
• Experience in clinical development or real world data.
• Experience with biomarker discovery and translation.
• Experience with text mining or competitor information analysis.
• Solid biology knowledge in Immunology, Neurodegeneration, and Oncology. 
• Excellent publication record.
• Willingness to travel less than 25% to conferences, offsites, customers, and internal meetings.

The US base salary range for this full-time position is expected to be $179k - $251k per year. Our salary ranges are determined by role and level. Within the range, individual pay is determined by factors including job-related skills, experience, and relevant education or training. This role may be eligible for annual discretionary bonuses and equity.