Immunoinformatics Engineer

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.

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.

We are seeking a detail‑oriented Immunoinformatics 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.

• 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.

• Master’s or PhD in Computational Biology (e.g., Bioinformatics) 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 bioinformatics AI research into production environments to transform drug discovery.

• 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.
  • Bioinformatics:
    Knowledge of biology data storage, formats, and protein‑structure prediction; proficiency with toolkits such as Bio Python.
  • Protein Dynamics:
    Experience applying MD techniques to protein‑protein interactions including using toolkits (e.g., OpenMM, Rosetta).
  • Integrative structural modelling:
    Experience integrating structural data, e.g. CryoEM or HDX‑MS with computational models in automated ways.
  • 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, Tensor Flow, 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.