Member of Technical Staff - Computational Biology

Member of Technical Staff - Computational Biology
A cutting-edge team is seeking a scientist to join a founding team focused on revolutionizing drug safety evaluation through the use of AI. This role involves building predictive models from biological data to improve the accuracy and speed of toxicity assessments, aiming to reduce dependence on traditional lab and animal testing. They're looking for individuals ready to embark on a bold and demanding journey, one that will test their limits, fuel their growth, and ultimately lead to meaningful breakthroughs and shared success.

This opportunity is ideal for a scientist who thrives at the intersection of biology, machine learning, and data science. You'll collaborate with a multidisciplinary team to analyze complex biological datasets and convert them into actionable insights. You'll also help design novel assays and work closely with external partners to accelerate therapeutic development.

Key Responsibilities

• Collaborate with ML researchers to enhance predictive model performance.
• Develop computational approaches to uncover mechanisms of toxicity at the molecular level.
• Investigate biological responses across various tissues (e.g., liver, heart, kidney, immune) in relation to chemical structures.
• Design and implement high-content imaging assays to generate training data for modeling.
• Lead the analysis of large-scale, multimodal datasets related to compound toxicity.
• Establish quality control pipelines for large imaging datasets.
• Translate complex biological signals into clear, actionable insights.
• Evaluate machine learning models to understand their behavior and limitations.
• Support external drug development teams in interpreting toxicity profiles.

Preferred Technical Skills

• Experience with high-throughput screening and automation platforms.
• Familiarity with high-content imaging and cell painting techniques.
• Proficiency in Python and data science tools (e.g., Pandas, NumPy, Jupyter).
• Background in imaging analysis tools such as CellProfiler and Cellpose.
• Strong foundation in statistics, curve fitting, dimensionality reduction, and machine learning.

Ideal Candidate Profile

• Passionate about building and analyzing biological datasets, especially from imaging.
• Strong programming skills, particularly for those with a biology background.
• Ability to bridge experimental protocols, computational workflows, and ML models.
• Deep curiosity across wet lab, dry lab, and drug discovery domains.
• Track record of scientific publications and effective communication.
• Commitment to scientific rigor, reproducibility, and data quality.
• Talent for identifying meaningful patterns in complex biological data.
• Drive to innovate beyond conventional methods in computational biology.

If you're driven by purpose, energized by challenge, and ready to help reshape the future of drug discovery through frontier pushing AI and computational methods, please apply.