Who are CellVoyant?

CellVoyant is a biotechnology company that predicts stem cell differentiation using live cell microscopy and artificial intelligence. We use this approach to optimise and unlock human tissue manufacturing for research and therapeutics applications. We aim to understand and solve important health issues, make a long-lasting positive impact on society and change the world.

We spun out from the Carazo Salas lab at the University of Bristol in 2022 and are backed by venture capital firms who were the earliest investors in DeepMind, Exscientia, Recursion, Wayve, and Abcam.


We are actively searching for a highly motivated and skilled Machine Learning scientist proficient in ML and computer vision, with a keen interest in genomics, microscopy data and their applications. As a Machine Learning scientist, your role will be pivotal in advancing the development of our AI models. This encompasses contributions to research, methodology development, data enhancement, and collection. You will be at the forefront of driving innovation in research and development at CellVoyant, leading to groundbreaking discoveries and creating new opportunities for pioneering research and publications.


As a ML Scientist you will be at the forefront of shaping the CellVoyant AI development with a focus on leveraging computer vision for microscopy data and collaborating closely on genomics-related projects. Your primary responsibilities include:
Pioneering Research Advancements: Identifying and leveraging state-of-the-art research papers and cutting-edge methodologies in machine learning to drive innovation in our AI models enabling predictive modelling, detection, classification, and pattern recognition of stem cell populations.

Data Enrichment: Identifying and sourcing open-source multi-modal data sets,(e.g., genomics and microscopy imaging), to enhance the robustness of our AI models. This involves collaborating closely with computational biologists to ensure data relevance and quality.

Data Strategy and Proof of Concept: Strategically identifying essential data requirements and developing data collection approaches to validate proof of concept for our AI models, particularly in the context of microscopy data analysis and genomics-related hypotheses.

Collaboration and communication: Collaborate with cross-functional teams, including computational biologists, biologists, data scientists, and software engineers, to drive multidisciplinary research projects. This involves fostering strong communication channels to effectively integrate computer vision techniques into genomics research endeavors.

Qualifications: A strong track record of research publications in top-tier AI conferences such as NeurIPS, AAAI, ICLR, ICML, CVPR, ECCV, IJCV, Ph.D. Degree in a technical subject (e.g. machine learning, AI, computer science, mathematics, physics, statistics).

Proficiency in programming languages like Python, cloud infrastructure (e.g., Google Cloud) and experience with relevant software (e.g., Github, Docker). Knowledge of ML/scientific libraries such as TensorFlow, PyTorch, NumPy and Pandas.Excellent analytical, problem-solving, and critical-thinking abilities. A collaborative mindset and the ability to work closely with computational biologists to identify hypotheses and validate proof of concept are essential for success in this role

Experience with Large-Scale System Design

Expertise in Self-Supervised ML and Generative Models such as Auto-encoders and GANs (Generative Adversarial Networks).Familiarity with Advanced Decision-Making Approaches: such as Deep Reinforcement Learning (DRL), Imitation Learning (IL), and learning from demonstrations.Expertise in Multi-Task and Multi-Modal LearningProficiency in Sequential Models such as Transformers.

Nice-to-have skills or background
(A big plus) Experience in genomics or bioinformatics research projects in an academic, pharmaceutical, or biotechnology setting.
(A big plus) Experience in microscopy data and their applicationsProven experience in stem cell research projects in an academic, pharmaceutical, or biotechnology setting.