AI Scientist - AI Molecular Design

Accountabilities: AI and Machine Learning Development: Support the design, development, testing, and deployment of machine learning and artificial intelligence models for molecular design applications, evaluating and implementing appropriate algorithms, tools, and frameworks. Molecular Design Innovation: Collaborate with cross-functional scientific and technical teams to develop AI solutions supporting protein and binder design, mRNA optimization, antibody optimization, and other molecular engineering initiatives. Algorithm Development and Optimization: Implement, adapt, and improve computational algorithms to address complex molecular design challenges, contributing to scalable and production-ready AI solutions. Protein Structure Prediction and Design: Apply advanced computational approaches and molecular modeling tools to support protein structure prediction, protein engineering, and binder design initiatives. Experimental Data Coordination: Coordinate activities across the assay-to-AI pipeline, working closely with experimental teams to support assay scheduling, execution, data collection, quality control, and reporting. Data Integrity and Standardization: Ensure experimental datasets are accurate, consistent, and properly standardized to support reliable AI model training, evaluation, and optimization. Wet-Lab and Computational Integration: Collaborate on Design-Make-Test-Analyze (DMTA) cycles, using experimental results to validate AI-generated molecular designs and improve model performance through iterative optimization. Model Evaluation and Continuous Improvement: Analyze experimental feedback and model outcomes to identify opportunities to refine algorithms, improve prediction accuracy, and enhance molecular design workflows. Technical Documentation: Document computational workflows, methodologies, experimental findings, and model performance to facilitate knowledge sharing and the integration of AI solutions into production environments. Cross-Functional Collaboration: Work with geographically distributed teams across scientific, computational, and business functions to understand project requirements and deliver effective AI-driven solutions. Research and Innovation: Stay informed about emerging developments in artificial intelligence, machine learning, computational biology, and molecular design, identifying technologies and methodologies that could improve research outcomes. Requirements: Education: Master's degree or PhD in Computational Biology, Computer Science, Physics, Applied Mathematics, or another relevant quantitative discipline, combined with practical experience in a related technical field. Programming and Technical Expertise: Strong hands-on coding and analytical experience with Python and machine learning frameworks, including Scikit-learn, PyTorch, TensorFlow, and RDKit. Algorithm Development: Demonstrated ability to develop, implement, test, and apply new or adapted algorithms through original coding to solve complex technical and business challenges. Production-Ready Solutions: Ability to translate scientific and technical requirements into scalable, reliable, and maintainable software solutions suitable for production environments. Protein Structure Prediction and Design: In-depth understanding of protein structure prediction and computational design algorithms, supported by practical experience modifying, extending, or improving tools such as Chai-1, Boltz-2, BindCraft, BoltzGen, Mosaic, or Proteina Complexa. Experimental Validation: Experience with wet-lab validation methodologies for AI-generated molecular designs, including Design-Make-Test-Analyze (DMTA) cycles and the use of experimental data to train and optimize AI models. Antibody Discovery: Familiarity with experimental approaches used in antibody or VHH discovery, particularly methodologies supporting hit identification and optimization. Scientific and Analytical Skills: Strong quantitative reasoning, problem-solving, and data analysis capabilities, with the ability to interpret complex scientific information and translate it into actionable computational solutions. Collaboration and Communication: Excellent communication and teamwork skills, with the ability to collaborate effectively across computational, experimental, and scientific disciplines, including geographically distributed teams. Adaptability and Continuous Learning: Curiosity and enthusiasm for emerging AI technologies, scientific innovation, and continuous professional development in a rapidly evolving research environment. Preferred Qualifications: Previous experience in the life sciences, diagnostics, or biotechnology industries, particularly within multidisciplinary teams. Working knowledge of agentic AI frameworks such as LangChain or LangGraph. Experience integrating large language models through Hugging Face, OpenAI, or Anthropic APIs. Familiarity with Next-Generation Sequencing (NGS) data and sequence analysis. Benefits: Competitive annual salary: Expected compensation ranging from $134,000 to $161,000, depending on experience, qualifications, and other relevant factors. Performance-based compensation: Eligibility for additional bonus or incentive compensation. Remote working flexibility: Fully remote work arrangement, allowing eligible employees to work from home in the United States. Healthcare coverage: Comprehensive medical, dental, and vision insurance for eligible employees. Retirement planning: Access to a 401(k) retirement savings plan. Paid time off: Paid leave to support personal needs, work-life balance, and time away from work. Professional development: Opportunities to work with advanced AI technologies, computational biology tools, and innovative molecular design methodologies. Collaborative scientific environment: Work alongside multidisciplinary experts contributing to developments in artificial intelligence, biotechnology, diagnostics, and life sciences. Meaningful impact: Opportunity to apply computational and scientific expertise to research initiatives with the potential to advance molecular engineering and healthcare innovation. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1