Computational Biology and Machine Learning Intern

Background: 

Recent advances in LLMs now enable rapid completion of previously time-intensive tasks. Flagship Labs 104 (FL104) is a privately held, early-stage company leveraging artificial intelligence and large language models (LLMs) to transform how we reason about complex phenomena. This is a unique opportunity to be at the forefront of scientific innovation, as we leverage cutting-edge advancements in AI and ML to drive breakthroughs in drug discovery and fundamental science. To improve our processes, we're exploring how genomic-technology enabled readouts can increase accuracy—and we're offering an exciting opportunity to contribute to this work. 

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 company's 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. 

Internship position: 

This role is a full time position aiming to start in June 2025. This position is a full time paid internship that requires the candidate to be on-site and fully available at the Cambridge location of Flagship Pioneering in Kendal Square. 

Responsibilities: 

• The candidate would be expected to deliver several prototypes of AI-supported tooling that can access large scale molecular profiling efforts alongside appropriate metadata. 

• These tools would then be evaluated by the candidate in isolation and in the context of agentic workflows to confirm the ability to access key molecular data in a structured and consistent manner. 

• Working with the wider team the candidate would set up appropriate benchmarking infrastructure to allow the evaluation and identify areas of improvement utilizing MLflow. 

• Following the development of these LLM-supported tools, the candidate would be expected to iterate on these tooling and functionality in the context of scaled AWS infrastructure with the support of FL104 

• Upon reaching a robust level of performance the candidate would work with software engineers and Machine learning engineers to bring the tooling into more production workflows in the context of powering a full stack solution. 

• The candidate would be expected to provide modular updates to the wider team throughout their time, upon completion of the project the candidate would be expected to give a presentation highlighting the work to the wider leadership team. 

Experience Required: 

• Experience with modern cellular and genomic technologies, particularly single-cell sequencing and spatial transcriptomics. Experience utilizing a standardized scRNA-seq toolkit such as ligand-receptor analysis, cell type enrichment, differential expression, etc. 

• Ability to analyze sequencing datasets to understand disease biology and evaluate target candidates using current methods. 

• Strong Python skills for analyzing bulk RNA-seq, single-cell, and spatial transcriptomics data. 

• Experience working with major genomic databases (GTEx, TCGA, Tabula Sapiens, DepMap, Human Protein Atlas, etc). 

• Up-to-date knowledge of current developments in scRNA-seq dataset analysis 

• Knowledge of drug binding and other biophysical components. Experience investigating these values from the literature and public databases. 

• Skill in translating biological questions into code and project plans, while converting data into meaningful insights for the wider team. 

• Interest in joining an innovative startup focused on improving the drug development process through artificial intelligence. 

Nice to have: 

• Proven ability to collaborate with machine learning scientists and software engineers including usage of MLflow. 

• Experience with version control (git) and cloud computing (AWS). 

• Knowledge of basic DevSecOps tools (Docker, Github-actions, AWS). 

• Demonstrated background in target discovery and validation (TIDVAL). 

Values and Behaviors: 

Flagship pioneers institutional and entrepreneurial innovation within an agile, startup environment. We seek candidates who possess an entrepreneurial mindset, excel at communication, and thrive in dynamic, cross-functional teams.