Careers
Build the Future of Drug Discovery
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Who we are
Peptris is an AI-first preclinical drug discovery company. We apply AI and computational tools to the challenge of designing new molecules for validated targets — bringing together biology, chemistry, and AI/ML under one roof. Our team is small, driven by curiosity, and believes the best drug discovery ideas come from people who can think across disciplines.
What this role is about
You will bring biological depth to a team that works at the intersection of biology, chemistry, AI, and data. Your job is to understand biology well enough to ask better computational questions — and to help ensure our AI models are solving real, meaningful problems. You don't need to be a coder first and a biologist second; we need someone who is deeply both.
What you'll do
- Work with protein structures — interpret protein structure data, assist with binding pocket analysis, and map key residues for drug interaction
- Integrate and analyse data from biological databases: PDB, UniProt, ChEMBL, Open Targets, STRING, and others
- Collaborate with AI/ML engineers to frame biological problems as computable tasks and validate model outputs against biological reasoning
- Build data pipelines and automation scripts for biological annotation and structure-activity analysis in Python and/or R
- Support structure-based and ligand-based drug design workflows — molecular docking, pharmacophore modelling, and virtual screening
- Stay current with scientific literature and bring relevant findings into active team discussions
- Touch on target biology, pathway context, and disease relevance where needed — but your core focus is molecules and structures, not omics pipelines
What you bring
- Bachelor's in Bioinformatics, Biotechnology, Biochemistry, or Life Sciences + MSc/MTech in Bioinformatics, Computational Biology, Computational Chemistry, or a related field
- Strong programming skills in Python — data wrangling, statistical analysis, visualisation
- Hands-on experience with structural biology tools: PyMOL, AutoDock, GROMACS, HADDOCK, or similar
- Familiarity with sequence and structure analysis tools: BLAST, HMMER, ClustalW, or equivalent
- Working knowledge of key databases: PDB, UniProt, ChEMBL, Open Targets, Reactome, KEGG
- Comfort with Linux/bash, Jupyter notebooks, Git, and reproducible research practices
- An instinct for biological plausibility — you can tell when a computational result doesn't make biological sense
- Interest in ML/AI and genuine curiosity about applying it to drug discovery problems
What we actually care about
- You understand biology deeply and can explain it clearly to non-biologists
- You're genuinely excited about what AI can do for drug discovery — not just in theory
- You're comfortable running an analysis you've never done before, guided by a paper and a hypothesis
- You see computational tools as creative instruments, not just pipelines to run
- You bring the team closer to the biology every time you open your mouth
- You can sit between a chemist and an ML engineer and help both of them
Who we are
Peptris is an AI-first preclinical drug discovery company. We use machine learning and computational chemistry to design new molecules for validated drug targets — faster, smarter, and more creatively than traditional methods. Our team is small, curious, and moves fast. We don't have a rigid playbook; we're writing it together.
What this role is about
You will build and experiment with the AI/ML systems that sit at the heart of our molecule design pipeline. You will work closely with computational chemists and biologists to translate research ideas into working models and tools — and you'll be expected to bring your own ideas to the table.
What you'll do
- Build and fine-tune ML/DL models for molecular property prediction, ADMET modelling, and de novo molecule generation
- Work with graph neural networks, transformers, and molecular foundation models applied to drug discovery tasks
- Develop pipelines that process and integrate chemical and structural data from public and proprietary sources (ChEMBL, PDB, PubChem)
- Collaborate on designing AI agents and multi-step workflows for autonomous molecule design and optimisation
- Prototype fast — experiment with ideas, fail early, and ship what works
- Read research papers and translate them into code; stay current with the ML and computational chemistry literature
- Contribute to internal tooling, reproducible research practices, and team knowledge-sharing
What you bring
- Strong Python skills — NumPy, Pandas, PyTorch or JAX, Scikit-learn
- Solid understanding of ML fundamentals: supervised/unsupervised learning, model evaluation, overfitting, hyperparameter tuning
- Familiarity with deep learning architectures — GNNs, transformers, or generative models
- Experience (even coursework or personal projects) with cheminformatics or computational biology is a strong plus
- Exposure to tools like RDKit, DeepChem, ESMFold, AlphaFold, or HuggingFace is a genuine bonus
- Ability to read papers and turn research ideas into working experiments
- Curiosity about biology — you don't need a biology degree, but you need to want to understand what the molecules are actually doing
What we actually care about
- You ask "why" before you ask "how"
- You're comfortable with ambiguity and can make progress without a fully defined spec
- You bring ideas, not just implementations
- You care about impact — not just clean code or accuracy metrics
- You're genuinely curious about biology and willing to learn it on the job
- You thrive in a small team where everyone's work is visible
Don't see a perfect fit?
We're always open to meeting curious, driven people. Drop us a line and tell us what you bring.
Get in Touch