Member of the Technical Staff, Biological Data

About the role

Output Biosciences has developed a biological reasoning model that comprehensively understands the complexities of biology. This model autonomously grasps the principles of molecular interactions, paving the way for innovative drug treatments that were previously unattainable. The company is already producing therapies that traditional methods cannot achieve. Here, you will have the opportunity to tackle some of the most challenging issues in AI and biology.

Key facts

What you'll do

• You will be responsible for curating training datasets that reflect the interactions of proteins and molecules, utilizing various biological data sources while applying your knowledge of molecular principles.
• You will devise strategies to enhance training data beyond existing public databases, employing biological and chemical reasoning to generate new training signals where data may be limited.
• You will create benchmarks based on real molecular phenomena, assessing whether our models have developed biologically relevant capabilities instead of merely statistical patterns.
• You will collaborate with model researchers to establish data strategies, determining the focus of model learning, prioritizing biological signals, and organizing the learning sequence across different modalities.
• You will design methodologies for integrating data across various biological scales and modalities, ensuring coherent training data from diverse experimental and computational sources.
• You will implement rigorous data splitting and evaluation strategies to prevent leakage and ensure that model capabilities are applicable to real-world biological challenges.
• You will keep abreast of advancements in biological data sources, experimental techniques, and molecular databases, consistently identifying new training signal opportunities.

Requirements

• You hold a PhD in computational biology, biophysics, structural biology, chemistry, biochemistry, or a related biological discipline, along with a minimum of 2 years of post-doctoral or industry research experience, or possess equivalent expertise through a combination of biology and computational skills.
• You possess a profound understanding of molecular interactions, protein structure, and biological data at the molecular level, grounded in fundamental principles rather than superficial knowledge.
• You have experience working with large biological or molecular datasets, including sourcing, cleaning, integrating, and analyzing diverse data.
• You are proficient in Python and capable of constructing computational pipelines for large-scale data processing.
• You understand the requirements of machine learning models regarding training data: coverage, quality, balance, and evaluation rigor.
• You view data construction as a research challenge rather than a mere task; you thoughtfully consider the meaning of data, the signals it conveys, and what might be lacking.

Nice to have

• Experience with computational biology tools such as structure prediction, molecular docking, or virtual screening.
• Background in training or evaluating machine learning models, especially with molecular or biological data.
• Publications in computational biology, bioinformatics, or molecular informatics.
• Familiarity with cheminformatics or molecular data analysis.
• Experience with protein or molecular language models.

Our Values

Heart: We cultivate a culture of ownership, assembling a team of individuals who take pride in their contributions.

Excellence: We are dedicated to maintaining high standards and continuously challenge ourselves to achieve excellence.

Practicality: We prioritize practicality and results-oriented thinking, aiming to make a significant impact on patients and the wider community.

Honesty: We value transparency and directness, believing in addressing issues openly as they arise.

Fun: We strive to create a workplace that is enjoyable, engaging, and fulfilling.

What We Offer

• An environment that encourages creativity, new ideas, and contrarian thinking.
• A constructive feedback culture to support your growth, with leadership maintaining high expectations while welcoming your input.
• Autonomy in managing your day-to-day responsibilities, with a focus on meeting milestones.
• Competitive compensation and equity in a well-funded startup.
• Comprehensive medical, dental, and vision benefits.