Cheminformatics Engineer

TN United Kingdom; London; On-site; GBP 60,000 - 100,000; Full time

Job summary

An innovative firm is seeking a Cheminformatics Engineer to drive transformative technology in drug design. This role emphasizes collaboration across disciplines, requiring expertise in AI methods and cheminformatics. The successful candidate will develop cutting-edge solutions, translating scientific advancements into practical applications. Join a mission-driven team dedicated to revolutionizing drug discovery and improving patient outcomes. If you are passionate about applying your skills in a fast-paced environment, this opportunity is perfect for you.

Qualifications

• Master’s or PhD in Computational Chemistry or AI.
• Proven track record of impactful contributions to scientific projects.

Responsibilities

• Build relationships with partners and deliver transformational projects.
• Collaborate with teams to integrate novel technologies.

Skills

Python

Java

C/C++

Communication Skills

Team Collaboration

AI Methods

Cheminformatics

Quantum Mechanics

Education

Master’s or PhD in Computational Chemistry

Experience in AI applied to scientific problem-solving

Tools

RDKit

OpenEye

PSI4

Orca

Gaussian

Apache Spark

Hadoop

AWS

GCP

Azure

Job description

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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.

Requirements

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.