Job Description

We are looking for a Computational Scientist (Biology) who works at the intersection of high-dimensional biological data, cell biology, and ML. You’ll help build predictive models from high-content imaging and functional biochemical assays to better predict human toxicity. The ideal candidate will have experience transforming noisy biological datasets into actionable insights, uncovering novel biological signals related to drug safety, and performing error analyses on ML models to understand biological mechanisms.

Culture:

• High-agency, high-intensity: They want people who see what matters and do it —no handholding, lots of initiative.
• Obsessive and irrational ambition: They admire candidates who could be in big tech but choose a harder path because they want meaning, challenge, and impact.
• Craft-first mentality: Deep mastery of your field—biologists who code, engineers who care about biology. You need to love the work itself .
• Curiosity-driven: They value people who dig into weird, niche hobbies and demonstrate intense curiosity across disciplines.
• Founding mindset: Think like an owner. They're building something from scratch and want people who act like it's theirs .
• Not for coasters: If you're looking for stability, predictability, or a 9–5 box-checker job, this isn't it.

Core technical responsibilities include:

• Analyze and interpret large-scale multimodal toxicity datasets.
• Develop custom algorithms to extract signals from high-content imaging, including Cell Painting and cell morphology data.
• Design and optimize novel assays that generate scalable, predictive datasets.
• Perform rigorous statistical analyses (curve fitting, dimensionality reduction, modeling, traditional imaging algorithms).
• Collaborate with ML researchers to enhance predictive accuracy by integrating biological insights and experimental data.
• Implement and manage quality control pipelines for high-throughput, imaging-based screens.

Preferred technical stack and experience:

• Python (Pandas, NumPy), Jupyter Notebooks
• Image analysis tools like CellProfiler and Cellpose
• Deep experience in assay development, high-throughput screening, and automation
• Experience analyzing imaging-based phenotypic screens or cell painting datasets
• Strong understanding of biology across systems (e.g., liver, heart, kidney, immune)
• Demonstrated ability to bridge wet lab protocols and machine learning pipelines

The right candidate is likely a biologist who taught themselves to code to handle their own data, with a passion for both technical rigor and biological discovery.

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