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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.
Duration
6-month probation prior to FTE is mandatory for the 1st time industrial experience candidates
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
Work arrangement
No
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