Machine Learning Research Scientist — Generative Antibody Design

mAIbe; Roma; Ibrido; EUR 60.000 - 85.000; Tempo pieno

Descrizione del lavoro

A biotech company in Italy is seeking a Machine Learning Research Scientist to develop generative models for antibody design. This role involves researching state-of-the-art architectures, prototyping algorithms, and collaborating with experts to ensure biological relevance. Ideal candidates should have a PhD in a relevant field and expertise in machine learning methods. Competitive compensation and a remote or on-site work environment are offered.

Servizi

Impactful problems addressing health challenges

Access to modern training infrastructure

Support for conferences and publications

Competenze

• PhD or equivalent research experience in a relevant field.
• Deep expertise in generative modeling and/or equivariant architectures.
• Strong programming skills in Python and experience with ML frameworks.

Mansioni

• Research and prototype new generative models for antibodies.
• Build representation-learning methods specialized to CDRs.
• Integrate ML-based scoring into training and sampling loops.

Conoscenze

Generative modeling

Deep learning

Python programming

Machine learning frameworks

Protein structure prediction

Formazione

PhD in ML/CS/Applied Math/Comp Bio/Physics

Strumenti

PyTorch

JAX

Descrizione del lavoro

🚀 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.

If you are passionate about generative AI, drugdiscovery, structural biology, computational immunology and chemistry, this is your chance to work at the very frontier.

🧬 The role

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.

This role combines deep theoretical understanding with hands‑on experimentation. 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.

What you’ll do

• Research & prototype new generative models for antibodies (e.g., sequence–structure co‑diffusion, flow‑matching with target conditioning, docking‑aware denoising).
• Build antibody PLMs and representation‑learning methods specialized to CDRs; integrate them as guidance signals for generation.
• Model binding and function: develop ΔΔG / affinity predictors from sequence and structure; integrate physics‑ or ML‑based scoring into training / sampling loops.
• Docking & complexes: combine AI with physics docking and learned rescoring for robust Ab–Ag pose modeling.
• Safety & developability: create immunogenicity, humanization, and multi‑objective optimization modules to balance affinity with manufacturability.
• 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.

🎓 Minimum qualifications

• 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.
• Solid grounding in at least one of: protein structure prediction, docking / scoring, molecular simulation, or biological sequence modeling.

Bonus points (nice‑to‑haves)

• Antibody‑specific know‑how: CDR definitions (Kabat / IMGT), numbering (ANARCI), SAbDab / OAS data, repertoire statistics.
• Experience with AlphaFold‑Multimer / AF3‑class models, Rosetta / PyRosetta, OpenMM, or docking tools (e.g., ZDOCK, ProPOSE).
• 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).

🌱 What we offer

• 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.
• Scholarly support: Backing for conferences, publications, and patents.
• Publication & OSS support: time and resources to publish and release tools.
• Growth & ownership: lead projects end‑to‑end, mentor interns / engineers, shape our roadmap.
• Competitive compensation and benefits.

We promise to

• Communicate clearly at every stage.
• Focus on what you can grow into—not just what you’ve done before.
• Be transparent with feedback and open to yours.

📍 Remote or On‑Site

🧬 Biotech meets AI

#AIjobs #ProteinDesign #AntibodyEngineering #MachineLearning #DeepLearning #ComputationalBiology #ComputerScience #BiotechCareers #Hiring