Bioinformatics Engineer – In Silico Antibody Design

Job Title: Bioinformatics Engineer – In Silico Antibody Design
Location: Foster City, CA
Duration: 12 months+ (possible extension)
 
Pay range: $60 - $63/hour on W2

Job Summary:
We are seeking a Bioinformatics Engineer with specialized expertise 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 (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 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.
 
 
 
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