Experience: Final year or post grad
Type: Full-time, On-site (Bengaluru)
About Mandrake Bioworks
Mandrake Bioworks is an AI-first biotechnology company reimagining how we design and engineer life itself. Our mission is to build intelligent systems that make biology programmable. We’re unlocking a new generation of gene editing technologies that will power breakthroughs in longevity, de-aging, sustainable agriculture, and climate resilience.
We’re building the foundational AI stack for biology - spanning large-scale biological data engines, foundation-model training, and generative design systems that can create new molecular tools from first principles.
If you want to build at the intersection of AI, life, and the future of civilization and see your models transform what humanity can engineer, this is the place to do it.
What You’ll Work On
- Build large-scale in-silico screening pipelines for protein discovery, optimization, and functional characterization
- Develop deep data-mining frameworks to extract, clean, and cluster large-scale genomic, proteomic, and metagenomic datasets
- Design and implement computational workflows for structure prediction, stability analysis, docking, and function annotation
- Run high-throughput virtual screening and mutational characterization to identify promising candidates for experimental validation
- Integrate structural biology tools with AI-driven models for activity prediction, structure-function correlation, and molecular scoring
- Collaborate closely with AI, data, and experimental teams to translate computational findings into validated molecular tools or therapeutics
You’re a Great Fit If You
- Have strong experience in computational biology, protein engineering, or structural bioinformatics
- Are fluent in Python / Biopython / PyRosetta / MDAnalysis / pandas / NumPy or equivalent scientific stacks
- Have worked with protein modeling and characterization tools (AlphaFold, Rosetta, FoldX, docking suites, or MD simulations)
- Understand sequence-structure-function relationships, active-site chemistry, and biophysical determinants of stability and activity
- Are comfortable handling large biological datasets and building reproducible data pipelines (Snakemake / Nextflow / Slurm / cloud)
- Have prior exposure to in-silico drug screening, virtual docking, or computational chemistry workflows