Who We Are:
Generate Biomedicines, Inc. is a Flagship backed, privately-held biotechnology company on a mission to reimagine the drug discovery process to one of dynamic, data-driven generation. We pursue this audacious vision because we believe in the unique and revolutionary power of generative biology to radically transform the lives of billions, with an outsized opportunity for patients in need. Generate will be successful by constantly turning innovative ideas into methods, technologies, and products that solve some of the most difficult challenges with developing medicines. We are seeking collaborative, relentless problem solvers that share our passion for impact to join us!
Generate was founded by Flagship Pioneering. Flagship Pioneering conceives, creates, resources, and develops first-in-category life sciences companies to transform human health and sustainability. Since its launch in 2000, the firm has applied a unique hypothesis-driven innovation process to originate and foster more than 100 scientific ventures, resulting in over $30 billion in aggregate value. The current Flagship ecosystem comprises 37 transformative companies, including: Moderna Therapeutics (NASDAQ: MRNA), Rubius Therapeutics (NASDAQ: RUBY), Indigo Agriculture, and Sana Biotechnology.
Machine learning-powered protein generation is at the core of Generate’s platform. We aim to upend the traditional approach to drug development towards one characterized by intentionality, surgical precision, and speed by developing methods for protein generation that can reliably generalize across biological functions, disease areas, and therapeutic modalities.
We are seeking a creative, motivated Machine Learning Scientist to develop and apply our core technologies for ML-powered protein generation. She/he will work with the ML group at Generate to develop innovative methods for protein generation and modeling, leveraging both in-house and external data to train and evaluate models while also deploying new algorithms into production on our experimental platform. The successful candidate will work closely with experimental scientists from Protein Sciences and Medicines groups to rapidly advance the scientific program.
- Develop novel machine learning models and algorithms for data-driven protein generation and hone them through direct deployment on our experimental platform.
- Advance and evaluate the state of the art for machine learning of protein sequence, structure, and function, including but not limited to protein sequence design, structure prediction, complex prediction, and function learning.
- Use our integrated data platform to devise models able to leverage measured labels “in-the-loop”.
- Work with Protein Sciences and Medicines groups to tailor modeling efforts toward high-impact therapeutic applications.
- Develop production-quality code in a team setting and work with MLOps for deploying and training models at scale.
- Present progress from scientific work in regular research meetings.
- PhD in Computational Biology, Computer Science or a related field with demonstrated experience working on biological applications.
- 3+ years of experience with developing innovative Machine Learning methods applied to biological problems, with a particular emphasis on protein modeling, design, or prediction.
- Evidence of innovation at the intersection of ML and protein science, immunology, organic chemistry, and/or genomics.
- Experience developing, debugging, and applying models using modern deep learning frameworks.
- Proficiency in Python and experience analyzing data with Numpy/Scipy, R, or similar.
Nice to Have:
- Practical experience with developing deep generative models (e.g., autoregressive models, VAEs, Flows, GANs, EBMs etc.) in an applied setting and knowledge of probabilistic machine learning foundations (i.e., probabilistic graphical models, linear algebra, differential calculus).
- Knowledge of optimization methods.
- Publications in major ML conferences or scientific journals that apply ML to problems in molecular biology, structural biology, or genetics, especially at the intersection of machine learning and proteins.
- Demonstrated experience developing software in a team setting.
- Experience with optimizing performant code.