(Senior) Scientist, Machine Learning at Flagship Pioneering, Inc. About the role Join a pioneering organization that is at the forefront of biotechnology innovation, dedicated to transforming healthcare and sustainability through groundbreaking solutions. As a (Senior) Scientist in Machine Learning, you will play a key role in developing and applying advanced computational methods to integrate proprietary data with machine learning techniques. Your work will directly support drug discovery efforts, platform development, and the creation of novel therapeutic modalities. This position offers an exciting opportunity to collaborate with interdisciplinary teams, leverage cutting-edge technologies, and contribute to impactful projects in a dynamic and innovative workplace. You will be involved in designing, benchmarking, and refining models that bridge quantum chemistry, proteomics, and medicinal chemistry, helping to accelerate the discovery of new medicines and chemical tools.

Key facts

- Location: Cambridge, MA
- Engagement: Full time
- Team: Expedition Medicines

What you'll do

- Develop, implement, and evaluate novel machine learning algorithms tailored to integrate quantum chemical features with proteome-wide engagement data, enabling better understanding of molecular interactions and biological activity.
- Design and execute comprehensive benchmarking protocols to assess the performance of models, including validation strategies such as cross-validation, hold-out testing, and real-world scenario simulations.
- Collaborate closely with medicinal chemistry teams to refine foundational models, ensuring they are aligned with experimental data and practical drug discovery needs. This includes translating model outputs into actionable insights for compound design and prioritization.
- Manage large and complex datasets, ensuring data quality, consistency, and reproducibility. This involves data curation, preprocessing, and storage, as well as developing pipelines for efficient data handling.
- Build scalable and robust data processing workflows to handle extensive molecular datasets, including experimental results, computational features, and chemical structures.
- Develop and optimize machine learning models, such as graph neural networks and other deep learning architectures, to predict molecular properties, biological activity, and chemical reactivity.
- Work in tandem with data engineers and software developers to improve modeling workflows, automate processes, and enhance data infrastructure for high-throughput analysis.




- Communicate findings clearly and effectively to multidisciplinary teams, including presenting model performance, insights, and potential applications in drug discovery projects.
- Stay current with advances in machine learning, quantum chemistry, cheminformatics, and related fields, applying new techniques to improve modeling approaches and outcomes.
- Contribute to the development of computational tools and platforms that facilitate chemical and biological data analysis, supporting the broader goals of the organization.
- Participate in scientific discussions, publish findings when appropriate, and contribute to the organization's knowledge base and intellectual property portfolio.
- Support the integration of covalent chemistry and chemoproteomics data into modeling efforts, expanding the scope of platform capabilities.
- Engage in continuous learning and career development to maintain expertise in cutting-edge computational methods and their applications in drug discovery.

Requirements

- Ph.D. in machine learning, computational chemistry, chemical physics, computer science, applied mathematics, or a related scientific discipline, with at least 2 years of relevant industry experience, or an M.S. degree with a minimum of 6 years of professional experience.
- Strong expertise in quantum chemistry, including density functional theory (DFT) and electronic structure calculations, with a solid understanding of their application in molecular modeling.
- Proven experience in developing and deploying molecular machine learning models, especially graph neural networks, and familiarity with generative models for chemical design.
- Demonstrated ability to collaborate effectively with medicinal chemistry teams, translating computational insights into experimental strategies.
- Experience in designing and implementing model evaluation frameworks, including validation and benchmarking protocols.
- Proficiency in Python and machine learning frameworks such as PyTorch, along with cheminformatics tools like RDKit and Gaussian.
- Familiarity with scalable data processing workflows, cloud computing, and high-performance computing environments.




- Knowledge of covalent chemistry and chemoproteomics is advantageous but not mandatory.
- Strong analytical skills, attention to detail, and the ability to handle large datasets efficiently.
- Excellent communication skills, both written and verbal, with the ability to present complex technical concepts to non-experts.
- A proactive, collaborative mindset and enthusiasm for working in a fast-paced, creative environment.

Nice to have

- Experience with advanced modeling techniques related to molecular design, such as generative models, reinforcement learning, or transfer learning.
- Familiarity with other computational tools and platforms used in drug discovery, such as molecular docking, molecular dynamics, or cheminformatics software suites.
- Background in covalent chemistry, chemoproteomics, or related fields that expand the scope of platform capabilities.
- Prior experience working in biotech or pharmaceutical industries, particularly in early-stage drug discovery or platform development.

Skills & tools

- Python programming language, with expertise in PyTorch and related machine learning frameworks.
- Cheminformatics tools such as RDKit for molecular manipulation and feature generation.
- Quantum chemistry software including Gaussian for electronic structure calculations.
- Graph neural network frameworks like DGL or PyTorch Geometric.
- Data management and processing tools for handling large-scale scientific datasets.
- Familiarity with high-performance computing environments and cloud platforms.
- Knowledge of covalent chemistry and chemoproteomics data analysis tools.

Practical notes

- The salary range for this position is between $132,000 and $258,500, with the final offer depending on the candidate's experience, skills, and qualifications.
- Expedition Medicines offers comprehensive healthcare coverage, retirement benefits, and an annual incentive program, among other perks.
- The role is based in Cambridge, MA, and requires on-site presence at the office.
- The company is committed to fostering a diverse and inclusive environment, welcoming applicants from all backgrounds.
- Candidates should be prepared for a rigorous interview process that may include technical assessments, discussions of previous work, and team fit evaluations.
- The organization values innovation, collaboration, and scientific excellence, providing opportunities for professional growth and contribution to impactful projects.

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📌 (Senior) Scientist, Machine Learning (Cambridge)
🏢 Flagship Pioneering
📍 Cambridge