The Opportunity

Insitro’s mission is to bring better drugs faster to the patients who can benefit most, through machine learning and data at scale. A key part of that mission is to rapidly identify safe and effective molecules that modulate novel targets that emerge from our biology discovery engine. Towards that goal, we have been building a cutting-edge, machine-learning-enabled capability to design and optimize small molecule therapeutics. Our work is enabled by proprietary experimental capabilities, including: generation of extensive binding, selectivity and affinity data via our second generation DNA-encoded library (DEL) platform; rapid synthesis to assay; ML-enabled measurement and analysis of high-content cellular phenotypes driven by small molecule perturbations; and the enablement of active learning via internal lab capabilities and extensive automation. In this role, you will collaborate with our world-class teams in drug discovery, software engineering, lab automation, and cell biology to build a unique approach for rapidly designing and optimizing small molecule therapeutics that have high efficacy, low toxicity and excellent pharmaceutical properties.

In this role, you will:

• Work collaborative with our Drug Discovery leaders on the strategy for deployment of advanced ML to accelerate and optimize small molecule drug discovery
• Lead and grow a team of outstanding machine learning scientists
• Guide your team to develop and deploy ML methods to analyze data from diverse small molecule data sets, including DEL binding data, pharmacological properties, and high-content functional readouts
• Onboard and develop ML-based molecular design methods, spanning from large data regimes (e.g., from DELs) to low-data regimes (few-shot or zero-shot), and including both predictive models and generative models
• Lead yearly and quarterly planning, set impactful goals, and align with cross-functional stakeholders
• Engineer robust, reusable platform components in partnership with the software engineering team
• Work with our chemistry and automation teams to design experiments that generate datasets that are fit for purpose for machine learning, including ones generated explicitly for training ML models
• Collaborate with the corporate development and strategy teams to assess potential external partners for molecular design collaboration and to acquire external data sets

You will be joining an exciting tech bio startup that has long-term stability due to significant funding, but that is still very much in formation. You will have ample opportunities for growth and impact. You will work closely with a diverse and talented team, learn a broad range of skills, and help shape insitro’s culture, strategic direction, and outcomes. Join us, and help make a difference to patients!

About You

• Ph.D. in computer science, chemistry, biochemistry, or a related discipline, or equivalent practical experience (e.g., a Masters degree plus 2 years in relevant industry experience)
• At least 5 years of industry experience in the field, including at least 2 in a line management or technical leadership role
• Peer reviewed publications in high-quality conferences or journals
• Demonstrated in-depth knowledge in the foundations and practice of modern machine learning, including deep learning, and their application to diverse molecular design tasks ● Considerable experience working with machine learning methods for small molecule chemistry across diverse tasks
• Demonstrated ability to lead a team of scientists and engineers to plan, execute and deliver a full machine learning solution for challenging, real-world problems: sourcing and qualifying training data; designing and implementing machine learning models; testing & benchmarking; shipping stable, robust, high-performance code.
• Extensive experience developing models using modern deep learning frameworks (PyTorch, TensorFlow, Keras, etc)
• Demonstrated ability in software engineering, including expertise in one or more general-purpose programming languages (such as Python, Java, Scala, C/C++, or Go) and experience with cloud computing (preferably AWS)
• Proven ability to mentor, coach, and lead junior scientists
• Ability to communicate effectively and collaborate with people of diverse backgrounds and job functions
• Passion for providing better medicine to patients in need

Nice to have

• Experience working on real world drug discovery campaigns
• Familiarity with non-ML methods for molecular design, such as traditional cheminformatics and molecular dynamics
• Exposure to ML methods in a low-data regime (few-shot or zero-shot learning)