Applied AI Scientist, Cheminformatics

Avenue Code is the leading software consultancy focused on delivering end-to-end development solutions for digital transformation across every vertical. We’re privately held, profitable, and have been on a solid growth trajectory since day one. We care deeply about our clients, our partners, and our people. We prefer the word ‘partner’ over ‘vendor’, and our investment in professional relationships is a reflection of that philosophy. We pride ourselves on our technical acumen, our collaborative problem-solving ability, and the warm professionalism of our teams.

Avenue Code has been believing in and promoting plurality actions for over 10 years, understanding that recognizing differences and fostering a safe environment, employment opportunities, representation, and support are the best ways to promote an increasingly equitable culture.

About the opportunity

Applied AI Scientist, Cheminformatics

Advances in AI, data, and computational sciences are transforming molecular design and development. Roche is leveraging these technologies to accelerate R&D, utilizing data and novel computational models to drive impact across our diagnostics and sequencing platforms. The "Gen-AI for SBX Chemistry" initiative is a strategic effort to harness the transformative power of generative AI to assist our scientists in exploring novel molecular structures and reducing design-to-test turnaround times.

We are seeking an exceptional AI/ML scientist with a strong background in computational chemistry and a deep interest in molecular foundation models and targeted molecule generation. Ideal candidates are motivated builders who can take ideas from AI research papers and translate them into robust, scalable in-silico models that predict molecular performance.

Qualifications

• PhD, pursuing a PhD degree (currently enrolled student) or equivalent advanced research experience in Computational Chemistry, Biophysics, Bioengineering, Computer Science, or a related technical field.
• Deep understanding of AI/ML methods specifically applied to molecular modeling and cheminformatics.
• Hands-on experience building and deploying generative AI architectures, specifically Transformers, Large Language Models (LLMs), Graph Neural Networks (GNNs), Diffusion models, Variational Autoencoders (VAEs), GFlowNets Reinforcement Learning Leraning (RL).
• Proven expertise and hands-on experience specifically in Property-Guided Molecule Generation.
• Proficiency in Python and experience writing clean, modular, and testable code using standard ML and cheminformatics libraries (e.g., PyTorch, RDKit).

Responsibilities

• Design and implement state-of-the-art generative AI pipelines to design novel small-molecule candidates optimized for specific performance metrics within our sequencing platforms.
• Design, train, and deploy advanced generative architectures for Computer-Aided Synthesis Planning (CASP), ensuring proposed molecules have highly feasible reaction pathways.
• Build automated machine learning models capable of predicting molecular performance phenotypes from 2D chemical structures, helping chemists prioritize or eliminate candidates prior to synthesis.
• Apply advanced few-shot learning techniques to combine molecular representations learned from massive public databases with Roche’s proprietary, high-quality datasets.
• Fine-tune public models on proprietary data for property prediction and to optimize relevant performance metrics.
• Work closely with experimental chemists and internal stakeholders to integrate in-silico predictions into applied AI frameworks used across our R&D pipeline.

Benefits

• Project commencement scheduled for June 1st, 2026.
• Competitive Compensation.
• Ownership of meaningful, business-critical applied AI projects that directly impact commercial sequencing instruments.
• Opportunity to work with experienced AI engineers, chemists, and ML practitioners in the biotechnology industry.