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.