Scientist II​/Senior Scientist, DEL Chemistry

The Opportunity

insitro uses advanced machine learning to analyze extensive biological and clinical data, aiming to improve the success rate of drug discovery and development. One of insitro's key platforms for generating small molecule therapeutics in neurological and metabolic diseases integrates DNA-encoded libraries (DEL) with machine learning. We are seeking a creative and data-oriented DEL synthetic chemist to lead lab-based DEL synthesis (on- and off- DNA) and data analysis.

This role will play a key part in the application of machine learning to enhance the effectiveness and speed of our preclinical drug development programs. This position's reporting structure will be dependent on your specific skillset and will require you to be in our South San Francisco headquarters five days per week.

• Design, develop, and validate DEL synthesis schemes with high structural diversity using robust chemistries that complement our machine learning platform
• Develop, validate, and implement innovative on-DNA chemistry strategies while providing technical expertise in synthetic methods
• Collaborate with our Medicinal Chemistry and Machine Learning teams to enhance DEL library portfolio, hit expansion strategies, and hit confirmation activities
• Interface with selections team in designing DEL selections to various protein classes
• Contribute to the analysis of DEL data and aid guidance of computational tool design and methodologies that strengthen DEL data analysis
• Support hit generation activities across therapeutic areas
• Perform the synthesis of DNA-Encoded libraries and hits (on-DNA) internally and oversee synthetic chemistry through external partners (off-DNA)
• Lead cross-functional teams in drug discovery campaigns, bringing together experimental and computational expertise
• Drive the implementation of machine learning approaches in DEL design, screening, and analysis

• PhD in, Medicinal Chemistry, Chemical Biology, Computational Chemistry or related discipline with 4+ years of relevant experience
• MS with 8+ years or BS with 10+ years of relevant experience will also be considered

• Exposure to, knowledge of, and preferably hands-on experience, across aspects of DNA-encoded Library technology, including:
  • Library design and synthesis strategy
  • DEL selection methodologies and optimization
  • Next Generation Sequencing (NGS) data handling
  • Advanced DEL data analysis and interpretation
  • Hit confirmation and validation strategies
• Proficiency with modern cheminformatics platforms, computational chemistry tools, and structure-based design approaches
• Experience in data visualization, statistical analysis, and interpretation of large chemical datasets
• Interest in integrating machine learning concepts with traditional DEL methodologies

• Demonstrated scientific impact through publications, patents, or successful drug discovery programs ideally leveraging DEL technology against diverse targets
• Proven contributions to hit generation, hit-to-lead or lead optimization campaigns

• Demonstrated leadership in driving scientific projects and mentoring junior scientists
• Successful track record of cross-functional collaboration with computational, biological, and chemical teams
• Experience working effectively with CROs and external partners
• History of professional engagement that advances both organizational goals and the broader scientific field

• Exceptional analytical thinking and creative problem-solving abilities
• Strong organizational skills with ability to manage multiple priorities in a fast-paced environment
• Outstanding communication skills to effectively translate complex scientific concepts across disciplines
• Ability to thrive at the intersection of experimental chemistry and computational approaches
• Enthusiasm for working in a data-driven, machine learning-focused drug discovery environment

Our target starting salary for successful US-based applicants for this role is $135,000 - $180,000. To determine starting pay, we consider multiple job-related factors including a candidate's skills,…