Applied AI Research Scientist

AQEMIA

. Design and implement novel deep learning algorithms, including GNNs, generative models, and physics-based models .

Posted 10/8/2026full-timeParis • FranceMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and implementing deep learning algorithms, particularly in molecular and biological contexts, while effectively collaborating with interdisciplinary teams to translate complex models into actionable insights.

Highest-signal resume keywords

Deep Learning AlgorithmsML Frameworks (PyTorch, TensorFlow, Scikit-learn)Python ProgrammingMolecular Property PredictionCollaboration with Chemists and Biologists

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Deep LearningMachine LearningRegression ModelsRanking ModelsGenerative ModelsPhysics-Based ModelsGraph Neural Networks (GNNs)Scientific Computing LibrariesData AnalysisModel Development

Soft Skills

Problem-SolvingAutonomyCollaborative Mindset

Certifications & Qualifications

MSc or PhD in Computer ScienceMSc or PhD in Machine LearningMSc or PhD in Computational Chemistry

Industry Keywords

Drug DiscoveryMolecular DataBiological DataCompound PrioritizationProtein-Ligand Interactions

Tech Stack

Tools & technologies

PythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

Design and implement novel deep learning algorithms, including GNNs, generative models, and physics-based models
Participate in cutting-edge research and bibliographic exploration
Collaborate with research and drug discovery teams to translate models into actionable insights
Develop robust ML models, including regression and ranking models, from molecular and biological data
Collaborate with chemists and biologists to drive compound prioritization
Own ML workstreams from goals and timelines through stakeholder communication
Deliver models and predictions for scientists’ everyday workflows
Predict molecular properties and protein-ligand interactions
Build ML models supporting internal and partnered drug discovery programs

Requirements

What you’ll need

MSc or PhD in Computer Science, Machine Learning, Computational Chemistry, or related field
Strong problem-solving skills, autonomy and a collaborative mindset
Approximately 3–5 years of experience applying ML in scientific or industrial settings
Strong experience with ML frameworks such as PyTorch, TensorFlow, and Scikit-learn
Solid Python programming skills
Experience working with scientific computing libraries

Benefits

Comp & perks

Flexible work arrangements including up to two remote days per week
Offices in the heart of Paris and London (King’s Cross)
Opportunity to work on cutting-edge drug discovery and new medicines
Collaboration with top pharmaceutical companies
Work alongside an interdisciplinary team of AI, physics, biology, medicinal chemistry, and software engineering experts
Opportunity to contribute expertise, learn from colleagues, and help shape the future of drug invention