Senior Scientist – Computational Biology & AI Modeling
About the Project BioLOCKEY is seeking an experienced computational scientist to lead the AI and computational biology component of a funded global health program focused on synthetic binder discovery and diagnostic development.
The program combines generative AI, protein structure prediction, computational protein engineering, physics-based modeling, and iterative DBTL optimization with experimental validation to discover high-affinity binders targeting biomarkers associated with cervical cancer, pre-eclampsia, and nutritional surveillance. The successful candidate will play a central role in designing and advancing computational pipelines that accelerate binder discovery while working closely with multidisciplinary experimental teams.
Key Responsibilities • Lead the computational biology and AI modeling strategy for synthetic binder discovery. • Design and optimize end-to-end computational protein engineering pipelines. • Drive AI-guided epitope mapping, binder generation, affinity optimization, and candidate prioritization. • Develop workflows integrating RFdiffusion, ProteinMPNN, AlphaFold, ESMFold, molecular docking, and molecular dynamics. • Lead development of scalable computational infrastructure and reproducible scientific workflows. • Analysis and optimization of bio-synthesis reactions using the power of AI/ML and computational modeling of underlying cellular processes. • Integrate computational predictions with experimental datasets to continuously improve AI models through DBTL cycles. • Mentor junior computational scientists and research associates. • Collaborate with structural biologists, protein engineers, molecular biologists, and diagnostic development teams. • Contribute to scientific publications, patents, technical reports, and grant deliverables. • Evaluate emerging AI technologies and incorporate them into BioLOCKEY's computational discovery platform. • Represent the computational biology team in scientific collaborations and project reviews.
Required Qualifications • Ph. D. degree in the field of computational structural biology or computational life sciences (e.g. bioinformatics, computer science, biophysics, computational biology, data science (AI/ML) or life sciences with strong expertise in AI/ML & computational protein design). • Experience in the implementation and application of ML-based methods for generative protein design. • 5–10 years of relevant research or industry experience. • Advanced programming skills in Python. • Strong experience with Linux, HPC, and scientific software development. • Proven ability to independently lead multidisciplinary research projects. • Excellent scientific communication and mentoring skills.
Preferred Technical skills Strong experience in several of the following: • Strong expertise in de novo design of functional proteins. • Deep understanding on the physical principles that govern protein folding and protein complex formation. • Deep expertise in AI/ML-based approaches for protein de novo design including application, training & fine-tuning of generative protein language models, diffusion- and flow matching approaches. • Knowledge of antibody/NANOBODY® structure, function & engineering. • Proficiency in Python and related libraries/software for de novo design (e.g. RFdiffusion, proteinMPNN, Rosetta, pyRosetta). • Ability to develop, benchmark and apply predictive algorithms for protein engineering. • Experience in handling, curating, and managing large biological data sets and data bases. • Expertise in developing and maintaining data pipelines and automated data workflows. • Comfortable working in cloud and high-performance computational environments. • GPU computing, Cloud-based scientific computing • Workflow orchestration and reproducible research
Nice to Have • Experience in antibody or nanobody engineering. • Experience in computational therapeutic or diagnostic protein design. • Knowledge of translational diagnostics and biomarker discovery. • Experience integrating AI models with experimental validation pipelines. • Experience managing collaborative multidisciplinary research programs. • Record of successful grant participation or international collaborative research.
Big Plus • Demonstrated leadership in AI-enabled protein engineering or computational drug discovery. • Publications in leading journals related to computational biology, AI, protein engineering, or structural biology. • Experience developing computational platforms that support experimental discovery pipelines. • Prior work on global health, infectious diseases, women's health, or point-of-care diagnostics. • Experience building or deploying generative AI workflows for real-world biomedical applications.