Machine Learning Research Scientist — Generative Antibody Design

TP-Link Italia; Roma; In loco; EUR 70.000 - 90.000; Tempo pieno

Descrizione del lavoro

A biotech company focusing on AI-based solutions is looking for a Machine Learning Research Scientist to innovate in antibody design. The role involves developing generative models and collaborating with protein engineers and immunologists. You will work on cutting-edge projects that combine AI with biology, with opportunities for publication and scholarly support. Candidates should possess a PhD in related fields and have strong skills in Python and deep learning frameworks.

Servizi

Impactful problems

Access to data and compute

Support for publications and patents

Competitive compensation

Competenze

• PhD (or equivalent experience) in machine learning or computational biology.
• Deep expertise in generative modeling methods.
• Strong Python programming and familiarity with deep learning frameworks.

Mansioni

• Prototype new generative models for antibody design.
• Develop ΔΔG/affinity predictors and integrate into the discovery pipeline.
• Collaborate closely with protein engineers and publish findings.

Conoscenze

Generative modeling

Python

Deep learning

Collaboration with wet-lab teams

Multi-objective optimization

Formazione

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

Strumenti

PyTorch

JAX

Docker

SLURM/K8s

Descrizione del lavoro

Machine Learning Research Scientist — Generative Antibody Design

🚀 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, drug discovery, 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.
• PhD (or equivalent research experience) in ML/CS/Applied Math/Comp Bio/Physics, with peer‑reviewed publicationsor 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.
• 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.
• Competitive compensation and benefits.
• 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.

🧬 Biotech meets AI

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

Seniority level

Mid‑Senior level

Employment type

Full‑time

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