Cheminformatics Engineer

Dalton; Greater London; On-site; GBP 55,000 - 75,000; Full time

Job summary

A leading AI drug discovery company in Greater London is seeking a Cheminformatics Engineer passionate about developing transformative technologies for drug design. The role includes building partner relationships, collaborating with diverse teams, and developing AI methods for product improvement. Ideal candidates will have a Master's or PhD in Computational Chemistry or AI, advanced Python skills, and a collaborative mindset to thrive in a fast-paced environment.

Qualifications

• Master’s or PhD in Computational Chemistry or AI applied to scientific problem-solving.
• Proven experience in impactful scientific projects.
• Advanced proficiency in Python or other programming languages.

Responsibilities

• Build relationships with partners and deliver transformational projects.
• Collaborate across teams to integrate novel technologies.
• Develop and integrate AI methods into robust products.

Skills

Python

Communication skills

Collaboration

Problem-solving

Education

Master’s or PhD in Computational Chemistry or AI

Tools

AWS

Python libraries (e.g., RDKit, TensorFlow)

Job description

About Dalton

Dalton is on a mission to make drug design more efficient. We are building the AI ecosystem for drug discovery, solving real-world problems, and transforming the pharmaceutical industry. Our goal is to harness cutting‑edge technology and turn it into impactful products for our partners. Join us as we revolutionize drug discovery and improve the lives of patients worldwide.

Why Join Dalton?

Dalton offers an exciting, collaborative environment where you can contribute to revolutionizing drug discovery. We value innovation, creativity, and commitment, and we are united by our mission to make a difference globally.

Role Overview

We are seeking a detail‑oriented Cheminformatics Engineer who is passionate about developing transformative technology to enhance the productivity of drug design. If you thrive in a fast‑paced, multidisciplinary environment and have the skills below, we’d love to hear from you.

Key Responsibilities

• Build strong relationships with Dalton’s partners and deliver transformational projects.
• Collaborate with cross‑functional teams—including data science, software engineering, and product development—to integrate novel technologies.
• Develop cutting‑edge AI methods and integrate them into robust products that improve partner efficiency.
• Deliver robust, extensible, and maintainable software solutions, translating high‑level business objectives into technical implementations.
• Drive your work with minimal supervision, managing the full software lifecycle from requirements capture to planning and execution.
• Stay informed about advancements in relevant scientific research and apply this knowledge to your work.

Capabilities

• Master’s or PhD in Computational Chemistry (e.g., Cheminformatics or Quantum Mechanics) or Artificial Intelligence applied to scientific problem‑solving.
• Proven track record of impactful contributions to scientific projects.
• Advanced proficiency in Python or experience with other programming languages (e.g., Java, C/C++).
• Strong communication skills, capable of conveying complex scientific concepts to diverse audiences.
• Inclusive team player, open to learning from others and contributing to a collaborative environment.
• Desire to rapidly transition novel chemical AI research into production environments to transform drug discovery.

Beneficial Skills and Experience

Expertise in one or more of the following areas:

• Artificial Intelligence: Experience with state‑of‑the‑art methods such as graph neural networks, transformers, Gaussian processes, generative modeling, or reinforcement learning.
• Cheminformatics: Knowledge of chemistry data storage, formats, and synthesis prediction; proficiency with toolkits such as RDKit or OpenEye.
• Quantum Mechanics: Experience applying QM techniques to synthesis prediction, including using QM toolkits (e.g., PSI4, Orca, Gaussian).
• Experience with data curation and processing from heterogeneous sources; familiarity with tools like Apache Spark or Hadoop.
• Proficiency with cloud platforms (AWS, GCP, Azure).
• Familiarity with major machine‑learning frameworks (e.g., scikit‑learn, TensorFlow, PyTorch).
• Open‑source contributions or publications demonstrating expertise in machine learning for scientific applications.
• Hands‑on experience with best software development practices in collaborative environments.