Laboratory Research Scientist

3 settimane fa

Piemonte, Italia mAIbe A tempo pieno

Help build the next generation of AI for antibody discovery.
mAIbe is advancing machine‑learning methods at the interface of protein science and therapeutics. We’re looking for a Machine Learning Research Scientist to invent and ship state‑of‑the‑art models for antibody design—spanning sequence–structure co‑design, docking‑aware generation, affinity prediction, immunogenicity/humanization, and end‑to‑end developability pipelines.
 
We’re hiring an ML Research Scientist to push the frontier of generative antibody design.
You’ll join a team of ML scientists and engineers from startups, industry, and academia to explore and extend SOTA architectures—including sequence–structure co‑diffusion, flow‑matching for flexible antigens, AI‑augmented docking, antibody PLMs, paratope/epitope inference, affinity prediction, and humanization/immunogenicity modeling.

You will design and prototype new algorithms, run careful experiments, and translate promising ideas into validated methods that advance our discovery pipeline. Partnering closely with protein engineers and immunologists, you’ll ensure model outputs are biologically interpretable and experimentally meaningful.
 
 
Research & prototype  new generative models for antibodies (e.g., sequence–structure co‑diffusion, flow‑matching with target conditioning, docking‑aware denoising).
integrate physics‑ or ML‑based scoring into training/sampling loops.
Own the loop:  design datasets/splits, run large‑scale training (cloud/HPC), and partner with experimental teams for prospective validation.
Publish & open source:  write papers, release code/models, and present at top ML/CB venues.
 
🎓 PhD (or equivalent research experience) in ML/CS/Applied Math/Comp Bio/Physics, with  peer‑reviewed publications  or strong open‑source track record.
Deep expertise in  generative modeling  (diffusion or flow‑matching) and/or  equivariant architectures  (SE(3), E(n) GNNs) or similarly impactful paradigms.
Strong  Python  +  PyTorch/JAX  engineering; comfort with large‑scale training, profiling, and distributed compute.
Antibody‑specific know‑how: CDR definitions (Kabat/IMGT), numbering (ANARCI), SAbDab/OAS data, repertoire statistics.
Graph/geometry tooling (PyTorch Geometric, e3nn), and experience with  multi‑objective optimization  for design.
MLOps: Docker, Weights & Biases, SLURM/K8s, cloud (GCP/AWS/Azure).
Cross‑functional collaboration with wet‑lab teams (assays, BLI/SPR, NGS libraries).
 
🌱 Impactful problems:  your models will impact real-world health challenges and directly influence antibody and vaccine design campaigns.
Serious compute & data:  access to curated antibody/antigen datasets and modern training infrastructure.
Growth & ownership:  lead projects end‑to‑end, mentor interns/engineers, shape our roadmap.
Communicate clearly at every stage.
Remote or On-Site
🧬

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