At Anagenex, we combine machine learning with massively parallel biochemical tools such as DNA Encoded Libraries (DELs) and Affinity Selected Mass Spectrometry (ASMS) to analyze more compounds more efficiently than ever before. By working with large datasets throughout our search process and letting our machine learning model guide our experiments, we are able to find molecules for the hardest problems in drug discovery, bringing first in class and best in class treatments to patients.

As a fully integrated team of experts in biology, chemistry and machine learning, we work as one to find new medicines. Collaborating across these divergent disciplines gives team members exposure and understanding into differing approaches for solving complex challenges. Autonomy and trust ground our flexible working environment, enabling us to succeed against a broad range of targets in drug discovery.

The Senior Machine Learning Scientist will contribute to the development of the Anagenex platform. Reporting to the Director of Engineering, this role will work closely with scientists in the laboratory and the machine learning team to iterate on our ML models and provide innovative computational solutions for the difficult research problems our team faces. A remote work environment supports the primary output of this role, with occasional visits to our laboratory facilities in Lexington, MA to facilitate live collaboration and an in-depth understanding of the work our laboratory team is conducting.

- The design, development, and implementation of machine learning algorithms using internally generated multibillion point datasets to power Anagenex’s platform

- Cross-functional collaboration with chemists, biologists, and data engineers to design Anagenex’s proprietary drug discovery technology

- Development of production-quality modeling code in a team setting

- Design and training of new classes of machine learning models to optimize new small molecule medicines

- Integrate diverse external and internal data sources including protein structure, cell based assays, animal based readouts, and other data to enable multi-objective optimization of small molecule medicines

- Mentor, learn, and share cross-disciplinary knowledge with others team members

- Present progress from scientific work in regular meetings and assist with reports and slide decks for broader internal and external communication

- Research the latest advancements in machine learning, artificial intelligence, and drug discovery fields, and proactively identify opportunities to apply new methodologies and technologies to the Machine Learning team’s work

- 3+ years work experience in Machine Learning with strong understanding of modern Deep Learning Architectures like Transformers and Graph Neural Networks

- Experience with applications in chemistry or small molecule drug discovery preferred but not required

- An advanced degree in computer science, machine learning, or life sciences such as chemistry or biology, or equivalent practical experience

- Expertise in machine learning, data mining, and statistical analysis techniques, with a proven track record of successfully applying these methods in real-world situations

- Understanding of the underlying math and statistical learning concepts behind modern machine learning techniquesFamiliarity with modern software development practices (Git, Issue tracking, code-review, CI/CD, etc)

- Ability to communicate and work in a complex, multi-disciplinary environment

- How a medicine goes from concept to helping patients in the real worldKnowledge of chemical informatics, molecular modeling, and drug discovery workflows

- Familiarity with Computational Chemistry methods

- Familiarity with nuances of Biochemistry research and associated ML challenges

- Publications or research in fields related to artificial intelligence and machine learning

- Participate in the onboarding program to learn about the scientific efforts fueling the - Anagenex platform, including DNA-encoded library (DEL) technology, computational chemistry, and medicinal chemistry

- Familiarize yourself with Anagenex’s tech stack

- Establish key relationships within the Machine Learning, Compute, and Scientific teams

- Begin exploratory data analysis for relevant targetsTrain your first models

- Understand the data sets you’ll work with and how they are generated in the wet lab

- Level up your understanding of the pipeline using full scale datasets to drive larger model training and inference

- Transform your insights into full-fledged quarterly goals

- Understand how to deliver machine learning results to the scientific lab team

- Give input on team goals and the prioritization of new projects

- Propose new projects and ideas to improve the ML output, and act on those proposal once approved

- Assist in preparing reports and presentations on ML progress to share at the biannual company onsite

- Possess deep insight into the iterative development of the Anagenex platform

- Demonstrate your expertise through major technical contributions, such as integrating new functionality in generative modeling or evaluating new architecture based on the latest research

- Advise on the feasibility of new developments and set realistic expectations for future deliverables