Job Title: Bioinformatics Engineer
Location: Foster City, CA
Duration: 12 months
PR: $50/hr - $65/hr
This would be a Hybrid role
Description:
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 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.
