Company Summary:

ProFound Therapeutics is discovering proteins hidden within the expanded human proteome to uncover novel drugs and drug targets, producing first-in-class medicines for a multitude of diseases. The company’s ProFoundry™ Platform uses state-of-the-art protein detection technologies to systematically identify and validate proteins and dissect their therapeutic potential. The result is an ever-expanding database of tens of thousands of novel proteins, including their connectivity, functionality, and roles in health and disease. ProFound Therapeutics was founded in 2020 by Flagship Pioneering. For more information, please visit www.profoundtx.com.

Position Summary:

ProFound Therapeutics is seeking a Machine Learning Engineer to expand its machine learning platform. ProFound is an early-stage biotechnology company working to revolutionize the human therapeutic landscape. We have discovered many completely new human proteins and have developed a unique dataset of their properties using a wide variety of computational and experimental tools. This constantly improving dataset provides the ProFound machine learning platform a unique and powerful resource to create and test innovative machine learning methods. The work the machine learning platform does has an immediate impact on ProFound’s ability to develop new, first-in-class therapeutics.

As a Machine Learning Engineer at ProFound, you will need to understand and implement several machine learning approaches, from logistic regression and tree-based algorithms, causal and graph models, to neural networks. You will develop, train, and evaluate a wide variety of machine learning models using in-house and external data. You will deploy successful models into production and integrate them closely with experimental platforms to develop feedback loops. To do so, you will collaborate closely with the machine learning and computational biology teams in a fast-paced environment that prizes rigor and independence.

Key responsibilities:

• Develop, train, and evaluate machine learning models to identify and prioritize novel therapeutic targets

• Develop a combination of research, and production-quality code in a team setting
• Ensure machine learning work is documented and reproducible
• Communicate results to computational biology and interdisciplinary audiences

Qualifications:

• Bachelor’s or master’s degree in computer science, data science, or machine learning with 2+ years of industry experience
• Alternatively, a PhD in computational life sciences with research focused on machine learning.
• Expertise in Python and machine learning frameworks such as Scikit-Learn, Pytorch/Lightning, Optuna, and HuggingFace.
• Expertise in git version control and reproducible code.
• Proven experience developing, debugging, and applying a variety of machine learning models in cloud environments.
• Desire to work across a developing data stack, from data ingest to model deployment.
• Desire to work on biological problems and learn from domain experts.
• Energetic self-starter with the ability to work effectively in a startup environment.
• Excellent analytical, communication, and presentation skills.

Values and Behaviors

We are seeking individuals with an entrepreneurial spirit, strong communication skills, 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.

At Flagship, we recognize there is no perfect candidate. If you have some of the experience listed above but not all, please apply anyway. Experience comes in many forms, skills are transferable, and passion goes a long way. We are dedicated to building diverse and inclusive teams and look forward to learning more about your unique background.

About Flagship

Flagship Pioneering is a biotechnology company that invents and builds platform companies, each with the potential for multiple products that transform human health or sustainability. Since its launch in 2000, Flagship has originated and fostered more than 100 scientific ventures, resulting in more than $90 billion in aggregate value. Many of the companies Flagship has founded have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.  Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies, and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.

If your experience is close to what we’re looking for, please consider applying. Experience comes in many forms, skills are transferable, and passion goes a long way. We know that diverse backgrounds and experiences make for the best problem-solving and creative thinking, which is why we’re dedicated to adding new perspectives to the team and encourage everyone to apply. We look forward to learning more about you.

Flagship Pioneering is 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.