Associate Scientist - Bioinformatics V
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Job Title: Bioinformatics Engineer In Silico Antibody Design
12+ months contract - Onsite at Foster City, CA
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 novel 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 (e.g., 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.
