Artificial Intelligence Intern

Location: Paullo

Cambrex Profarmaco Milano is looking for a curious and dynamic intern interesed in Artificial Intelligence.

For our Site in Paullo (Milan) we are looking for a trainee interested in with an expertise in ML methods and AI to insert in our Analytical Development team for an industrial secondment focused on building an AI-powered HPLC recommendation engine. This project leverages proprietary pharmaceutical data to develop machine learning models that predict optimal chromatographic conditions for new compounds.

Responsibilities

Design and curate a structured experimental knowledge database linking molecular representations (SMILES, InChI, fingerprints), physicochemical descriptors, chromatographic method parameters, and experimental outcomes, with a focus on data quality, reproducibility, and design suitable for ML;

Develop a feature selection pipeline for classical descriptors (RDKit, Mordred) and learned representations (molecular fingerprints, graph-based embeddings);

Research, train, and benchmark predictive models for chromatographic outcomes (retention behaviour, mobile phase strength, column selectivity class), exploring both interpretable models and state-of-the-art approaches;

Design a molecular similarity module grounded in chemical space geometry, evaluating distance metrics and embedding spaces for nearest‑neighbour method retrieval from historical data;

Build a recommendation engine that unifies predictive modelling and the similarity module, with uncertainty quantification, confidence scoring, and explainability to support trust and adoption by domain scientists;

Extend the engine into an agentic LLM interface that allows natural language interaction with the underlying models and database;

Validate the system on held‑out experimental data, document methodology to publication standard, and present research outputs to both technical and domain‑expert audiences.

Qualifications and Skills

Degree in Computer Science/Engineering or a closely related field;

Deep understanding of ML and AI.

Strong research instincts: ability to identify the right problem formulation, design controlled experiments and critically evaluate model behaviour rather than just benchmark metrics;

Proficiency in Python and relevant libraries (scikit‑learn, pandas, Num Py, PyTorch, Tensor Flow), comfort reading and adapting research code.

Soft Skills

Excellent interpersonal and communication skills.

Ability to work effectively in a team and flexibility;

Proactivity, a strong focus on results, and problem‑solving skills;

Ability to work independently and communicate technical results to a non‑specialist audience.

It would be considered a plus

Familiarity with molecular representations and cheminformatics tools (SMILES, fingerprints, graph neural networks for molecules) or willingness to learn;

Active interest in explainable and interpretable ML (XAI), particularly in applied scientific contexts where trust and transparency are critical;

Hands‑on experience with LLM tool‑use, function calling, or agentic frameworks or conceptual grounding in how LLMs interact with external systems;

Exposure to scientific, industrial, or experimental datasets with inherent noise, class imbalance, or sparse labelling, common in real‑world R&D settings.

What You Will Gain

Access to a proprietary industrial HPLC dataset not available in academic settings;

AI research challenge with real-world constraints;

Immersion in cheminformatics and pharmaceutical analytical R&D, with direct collaboration with domain scientists who will challenge and sharpen your modelling decisions;

Research outputs aligned with your doctoral trajectory: publishable methodology, a working system demonstrating scientific AI in industrial settings;

Mentoring from both chemoinformatics and domain experts, with genuine intellectual exchange;

Professional networking opportunities.

Contract
We offer 1 year scholarship contract, details will be clarified during the interview process.

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