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Scientist, Interpretable Machine Learning - Montai Health

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Job Title
Scientist, Interpretable Machine Learning
Job Location
Cambridge, MA USA
Job Description

About us

Montai Health is a privately held, early-stage biotechnology company within the Flagship Pioneering Portfolio founded in 2019. Montai Health’s mission is to improve health outcomes for as many people as possible by unlocking the power of natural bioactives to more rapidly develop safe and efficacious therapies to treat and preempt disease. We do that through our CONECTA™ platform which comprehensively maps Anthromolecule bioactivity across pathways that drive disease. The CONECTA platform leverages rapid and ever-growing advancements in the digitization of natural chemistry and human biology coupled with powerful computational tools for linking chemical structure and bioactivity. This confluence of biotechnologies enables Montai to build a powerful, one-of-a-kind discovery platform focused on creating safe, effective, and more human-centric medicines than ever before.

We know that pioneering is an expedition.  Our values reflect our mission and our culture:

  • Human driven pioneering
  • Relentless hunger for impact
  • Interdependently ambitious
  • Inclusive courage

Please join us on our expedition! For more information, please visit

Position Summary

We are looking for a machine learning scientist specializing in explainable artificial intelligence (XAI) or interpretable machine learning (IML) to join our small and growing team of ML scientists & engineers. This person will work with and contribute to an innovative set of machine learning models and tools built on top of a robust ML engineering infrastructure. They will build valuable experience in:

  • Domain-specific applications of industry-leading modeling approaches including graph neural networks, transformers, and fully Bayesian models
  • Drug discovery and the pharmaceutical industry
  • Applied machine learning for a range of biological data modalities including in vitro and in vivo data, clinical data, and multi-omic data



  • Enable understanding of the real world behavior of ML models, revealing their ability to transfer or encode bias on novel datasets, and inform scientific understanding of modeled datasets
  • Contribute to the design of deep learning and other computational models such that they are built with interpretability in mind
  • Develop and implement post-hoc interpretability metrics that can be used to understand a range of chemical/biological machine learning models previously used in a ‘black box’ paradigm
  • Implement exploratory analysis tools for abstractions such as feature visualization and attribution that aid scientists in interpreting and explaining machine learning model results
  • Collaborate with our Data team to deploy software tools for model interpretation and explanation usable by chem/biological domain experts

Basic Qualifications:

  • Advanced degree in computational biology, machine learning, computer science, statistics or a related field OR bachelor with a minimum of 5 years of applied experience with AIX, IML, or related model interpretability methods. Advanced degree may count toward years of experience.;
  • A minimum of 3 years of software engineering experience oriented towards interactive data visualizations for ML models
  • Familiarity with python development for interactive data visualization tools using frameworks such as Panel, Streamlit, Dash, Jupyter, or Voila

Additional Qualifications:

  • Highly collaborative team member eager to work with experts from multiple domains (biology, chemistry, statistics, ML, software engineering)
  • Excellent communicator able to convey complex technical ideas across disciplinary boundaries
  • Demonstrated ability to build and deliver ML model interpretability tools usable by technical experts and non-technical stakeholders
  • Web front-end / UI development experience with frameworks such as React or Svelte, and visualization libraries like d3 or p5
  • Familiarity with deep-learning-oriented parameter/metric monitoring and visualization toolkits, e.g. Tensorboard or Weights & Biases
  • Familiarity with model design, implementation, debugging, and testing in PyTorch
  • Experience with ML applications in biology, chemistry, medicine, the life sciences, or a related natural sciences field

Values and Behaviors

We are seeking individuals with an entrepreneurial spirit, strong communication skills, outstanding organization skills and impeccable record keeping, and comfort working in and contributing to a dynamic and cross-functional team environment. The level of the role will be commensurate with the education and years of experience of the identified candidate. 

Flagship Pioneering and our ecosystem companies are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.

Montai Health Headquarters Location

Cambridge, MA

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Montai Health Company Size

Between 20 - 50 employees

Montai Health Founded Year


Montai Health Total Amount Raised


Montai Health Funding Rounds

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  • Series A

    $50,000,000 USD