Associate Scientist - Bioinformatics V*

Job Title : Bioinformatics Engineer In Silico Antibody Design

We are seeking a Bioinformatics Engineer with specialized expertise in in silico antibody design to lead the development of an AI-first platform, transforming the future of antibody-based drug discovery.

This pivotal role requires an innovative and proactive individual with deep experience in bioinformatics, machine learning, and large-scale biological data, particularly in antibody design and optimization.

The ideal candidate will be responsible for building scalable AI-driven solutions that accelerate the identification, validation, and development of therapeutic antibodies.

Key Responsibilities :

• Develop AI-Driven Antibody Design Ecosystems : Design and build advanced platforms to drive in silico antibody design and optimization, supporting rapid and efficient therapeutic discovery.
• Implement Scalable Antibody Prediction Models : Architect machine learning models specifically tailored for antibody sequence and structure predictions, leveraging deep learning to predict binding affinities, structural stability, and therapeutic potential.
• Leverage Cloud Platforms for Antibody Data Processing : Utilize modern cloud platforms for large-scale data processing, storage, and computation, ensuring the scalability of antibody design pipelines.
• Apply State-of-the-Art AI Techniques for Antibody Discovery : Innovate with cutting-edge AI methods, including diffusion models and neural networks, to refine antibody sequences and explore vast design spaces for Client therapeutic candidates.
• Collaborate on Antibody Drug Discovery : Work with cross-functional scientific teams to integrate data from immunology, structural biology, and bioinformatics into actionable insights for antibody discovery and optimization.
• Continuously Integrate Emerging Technologies : Stay ahead of AI and bioinformatics advancements, continuously refining and expanding in silico methods for antibody engineering and drug discovery.

Minimum Qualifications :

• Educational Background : PhD in Bioinformatics, Computational Biology, Computer Science, or a related field, with demonstrated expertise in antibody design.
• Machine Learning Expertise : Solid experience applying AI and machine learning frameworks to biologics, particularly antibody data.
• Programming Proficiency : Proficient in Python, R, and experience with bioinformatics libraries (, Biopython, PyMOL), with strong skills in cloud-based deployment of machine learning applications.
• Experience with Antibody Datasets : Demonstrated expertise in handling antibody sequence and structural data, and applying machine learning to improve therapeutic properties such as affinity, specificity, and stability.

Preferred Skills :

• Data Handling Expertise for Antibody Design : Extensive experience in curating, harmonizing, and preprocessing large-scale antibody datasets, including high-throughput screening data and structural models.
• Understanding of Antibody Data Nuances : Deep understanding of antibody sequence-structure relationships, developability challenges, and immunogenicity risks, with an ability to integrate these insights into data workflows.
• Advanced AI Methods for Antibody Engineering : Experience with AI-driven techniques such as inverse folding, generative models, and structural docking to guide antibody design and optimization.
• Analytical and Strategic Skills : Strong analytical abilities to extract actionable insights from complex antibody datasets, with a focus on developing innovative therapeutic strategies.
• Collaboration and Communication : Proven ability to collaborate in agile, interdisciplinary teams and communicate effectively across scientific and technical domains.
• Passion for Innovation in Antibody Therapeutics : A passion for driving the next generation of antibody therapeutics through AI, accelerating drug discovery timelines and improving clinical outcomes.

EEO :