Data Science Co-Op

Flagship Pioneering Co-Op ProgramCambridge, MA
$25 - $40

About The Position

Pioneering Intelligence (PI) is a Flagship initiative focused on developing and harnessing AI/ML based platforms for innovative applications in life sciences and beyond. We are looking for extraordinary computational scientists and engineers to work alongside our machine learning team to solve the most impactful challenges in AI for the life sciences. Our Co-ops for will work on challenging problems in machine learning and computational science for the life-sciences. They will work closely with a team of interdisciplinary scientists consisting of ML and domain experts to advance the state-of-the art in machine learning and contribute to our ongoing efforts. This is an exciting opportunity to be part of a fast-paced, highly dynamic entrepreneurial environment. We work on problems and use techniques like: Generative models for biomolecular design including flow matching, diffusion, and language models. Self-learning agentic systems. Biophysical simulation.

Requirements

  • Fluency in Python and experience implementing ML methods with standard tools and packages (e.g., PyTorch, JAX).
  • Readiness to deep dive into the biology/physics/chemistry of the platform companies we build

Nice To Haves

  • Publications or experience in CS, ML, Computational Chemistry/Biology or a similar computational field.
  • Experience with generative models, natural language processing, graphical neural networks, reinforcement learning, or similar.
  • Familiarity with software development, including git and public cloud computing resources (e.g. AWS, GCP, Azure).

Responsibilities

  • Work with an interdisciplinary team of scientists to build models, organize data and deliver computational solutions.
  • Experiment with diverse datasets and novel techniques.
  • Communicate your work to a multi-disciplinary audience.

Benefits

  • healthcare coverage
  • annual incentive program
  • retirement benefits
  • a broad range of other benefits
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