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Sr. Scientist, Machine Learning

Fog Pharmaceuticals

Fog Pharmaceuticals

Software Engineering
Cambridge, MA, USA
Posted on Tuesday, July 2, 2024

Why join us?

FogPharma is a biopharmaceutical company pioneering the discovery and development of Helicon™ therapeutics, which are peptides capable of efficient cell entry and modulation of both protein-protein and protein-DNA interactions. Through Helicon therapeutics, FogPharma is poised to revolutionize the medical possibilities for patients by precisely drugging intracellular targets long understood to be significant drivers of disease but never before drugged due to the limitations of existing drug modalities to act within the cell.

FOG-001, the company’s first-in-class TCF-blocking β-catenin inhibitor, is being evaluated in a Phase 1/2 study for patients with advanced solid tumors, including colorectal cancer. FogPharma is fully leveraging the unprecedented potential Helicons present by deploying proprietary, custom-built machine learning and computational methods as part of its discovery and development process. FogPharma has raised more than $500 million to date from leading life sciences investors. FogPharma is headquartered in Cambridge, Mass.

What’s the opportunity?

This position is a Senior Scientist, Machine Learning reporting to the AD of Machine Learning, in Computational Drug Discovery at Fog. We are seeking highly talented and motivated people to contribute to the development of the Computational Drug Discovery group, a strategic function in Data Science that is part of Fog’s platform discovery engine for HeliconTM stapled-peptide drugs. The skill sets of the group includes state-of-the-art machine learning/generative AI, molecular modeling, cheminformatics, structural sciences and data science towards the discovery and development of HeliconTM stapled-peptide drugs.

You’ll be part of a data science team that is a central pillar of FogPharma’s innovative discovery platform and pipelines targeting “undruggable” genes of major therapeutic interest to patients. Our data science team is an integrated team ranging from computational biology, bioinformatics, computational drug discovery, research informatics and data engineering. We work at the interface of chemistry, biology, clinical and computational sciences, and are responsible for all aspects of data science from building the discovery pipeline to supporting and developing our discovery platform.


  • Provide scientific leadership and machine learning (ML) expertise for, but not limited to, Helicon hit identification, targeted screening library designs, hit-to-lead progression using multi-objective optimization, new drug-target assessments and advancing drug-pipeline projects towards clinic.
  • Develop foundation models leveraging Fog’s in-house data along with suitable related data sets
  • Develop and implement novel machine learning methods for predicting HeliconTM properties and functional outputs.
  • Support, develop and productionize our internal protein generation efforts and streamline nominations of HeliconTM designs for downstream experiments.
  • Evaluate and, where appropriate, adopt and fine-tune state of the art protein-based ML models for our internal HeliconTM-to-function modeling efforts.
  • Deploy, manage, and optimize ML models in production, using MLOps capabilities.
  • Employ rigorous data science and software engineering best practices in your day-to-day work.

Establish a strong cross functional collaborative-network within Fog and with external partners

What you’ll need to be successful:

  • PhD or M.S. in Computational Biology, Computer Science, or related scientific field with 2+ (PhD), or 4+ years (M.S.) of industry experience, developing and applying machine learning models within the pharmaceutical or biotechnology or related field.
  • Experience in modeling biological sequence/structure-to-function relationships and collaborating with experimental biologists to validate predictions is a plus.
  • Demonstrated understanding of critical assessment of data procurement, data preparation, model training and predictive model quality.
  • Expertise/specialization developing natural language, or graph-based models from scratch.
  • Practical experience developing deep generative models (e.g., autoregressive models, VAEs, Flows, Diffusion, or Transformers)
  • Experience in active learning approaches is a plus.
  • Fluency in PyTorch or Tensorflow or JAX and scikit learning is mandatory. Familiarity in specialized ML libraries (e.g. Deepchem) a plus.
  • Fluency in python scientific/data stack (pandas, numpy, scipy etc.) is mandatory

FogPharma is a team of passionate pioneers who are trailblazing the future of precision medicine with the aim of making a meaningful difference in the lives of patients. The company is committed to promoting an inspiring and flourishing working environment for all employees across the business, in all departments, and driving innovation for patient benefit.

  • Creative. We are creating a whole new class of medicine requiring creativity to solve challenges as they arise, which we have successfully done since the inception of the company.
  • Patient-focused. We are deeply focused on patient outcomes, and all energy in the company is focused on science as it translates to patient impact.
  • Execution-oriented. As we begin clinical development and large-scale manufacturing, the team is balancing creativity and nimbleness with relentless, rigorous and flawless execution.
  • Humble. We fully appreciate that science and technology and policy are in flux, and we balance deep experience with humility to ask fundamental questions and seek newly available solutions.

As an equal opportunity employer, Fog values diversity and welcomes applicants of all backgrounds and experiences. All qualified applicants will receive consideration for employment without discrimination on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other factors prohibited by law.

30 Acorn Park Drive | Cambridge, MA 02140 |