BioAI Research Engineer (All Levels)

Key Skills

Aartificial intelligence, Data Analysis, Data science techniques, Data Visualization, Deep Learning, Linux Operating system, Machine learning techniques, Python Programming, PyTorch, TensorFlow

Job Description

In the BioAI department, we advance the boundaries of healthcare and medical science through a combination of biology and artificial intelligence expertise. We are expanding a portfolio of successful initiatives across drug discovery, design, and protein engineering. Notably, we are developing the next-generation vaccines and biopharmaceuticals for cancer treatment and prevention and therapy of infectious diseases. By joining us, you will contribute to this effort in collaboration with scientists, engineers and biologists from both InstaDeep and BioNTech and have the opportunity to contribute to scientific papers submitted to leading conferences and journals.

Specifically, we work on the following research directions, and apply them at high scale in active production applications.

RNA Science: We are pushing forward the state of the art in optimisation of RNA for multiple characteristics including expression and half life in media and in cell. We are developing advanced methods of RNA characterisation and prediction of secondary and tertiary structure.

DNA Science: We are developing models of DNA chemistry to predict characteristics such as folding, binding energy and melting temperatures. We apply these models in areas such as biomolecule synthesis optimisation.

Immunogenicity: We are building models to understand the mechanisms of immunogenicity in order to design personalised immunotherapies.

Protein Folding: We are designing, utilising and improving the state-of-the-art protein folding models (e.g. AlphaFold 2, RFDiffusion, etc.) to investigate protein structural conformation and its distribution. These folding models are being used to understand protein functionalities, estimate protein stability, and study protein mutations and structural changes.

Protein Design: We are leveraging structural modelling, protein language models, protein folding models and quality-diversity methods to perform protein design with experimental feedback from wet labs.

Protein-protein Interactions: We are developing predictive models, based on molecular dynamic simulations and protein folding models, to study protein-protein interactions, dissociation kinetics, binding affinity enhancements and immune response activation.

Infectious Disease Modelling: We are actively monitoring the evolution of SARS-CoV-2 using structural modelling, DNA/protein language models, and protein folding models, with a direct impact on epidemiological studies and vaccine developments.

Responsibilities

• Follow and communicate the latest developments in machine learning and biology. Design, implement and deliver performant and scalable algorithms based on state-of-the-art machine learning and neural network methodologies using distributed computing systems on-premises and cloud infrastructures.
• Conduct rigorous data analysis and statistical modelling to explain and improve models.
• Report results clearly and efficiently, both internally and externally, verbally and in writing.
• Write high-quality, maintainable, and modular code together with precise documentation.
• Actively collaborate with the business development team in the pre-sales activities, including but not limited to presenting the company to new prospective clients, writing decks and proposals, participating in calls and meetings, and representing InstaDeep in conferences/events.

Requirements

• Master's, PhD degree or equivalent experience in applied mathematics, computer science, or related scientific field.
• 1+ year experience in deep learning demonstrated via previous work, publications, contributions to open source projects, or coding competitions.
• Strong software engineering experience (Python, Docker, Linux).
• Strong experience using a machine learning framework (PyTorch, JAX, TensorFlow).
• Strong desire to work with biological applications.
• Data science and statistics experience including data visualisations, statistical testing, etc.
• Excellent communication skills in English.
• Appropriate work permit for the considered location.

Desirable

• Knowledge of molecular biology, structural biology, -omics, immunology, or a related discipline.
• Knowledge of current research in deep learning applied to biology.
• Specialist computing knowledge, such as high-volume data storage and processing, high-performance computing, or deployment.

Desirable at Senior Level

• 5+ years professional or academic experience in machine learning.
• Experience setting direction in machine learning research projects.
• Experience with and desire to mentor colleagues.
• Experience managing projects or leading teams.
• Experience bringing machine learning research to production.