Research Scientist | Generative Models | Foundational Models | Diffusion | LLMs |Biology

Research Scientist | Generative Models | Foundational Models | Diffusion | LLMs |Biology

Company Overview

Founded in 2014, our company is a leading AI innovator at the forefront of the industry. With offices in major cities worldwide, we collaborate with top tech companies and prestigious academic institutions. We are proud to have been recognized among the fastest-growing AI companies globally. Our recent acquisition by a major biotech firm has further solidified our leadership in the AI space.

The Team

We are seeking Research Scientists to join our Research Team, based in one of our major office locations, working at the cutting edge of generative AI and its applications in life sciences. Our team is dedicated to advancing foundational AI research and developing the next generation of deep learning technologies. Opportunities are available for both recent graduates and senior professionals.

Generative foundation models are transforming computational biology and drug design, and our company is leading the charge in developing new technologies and applications. This involves pushing the boundaries of generative AI with innovations in algorithms, enhanced sampling, and reasoning techniques, and validating these ideas for biological domains.

As a Research Scientist, you will have the opportunity to:

• Advance Foundational Research:Tackle challenges in generative modelling, such as handling heterogeneous, multimodal, and noisy data, and developing effective sampling algorithms for both conditional and unconditional generation.
• Develop Biological Foundation Models:Contribute to the creation of new models that advance the possibilities in computational biology.
• Collaborate Across Disciplines:Work closely with Machine Learning Engineers, Research Engineers, Computational Biologists, and other experts.
• Leverage Cutting-Edge Resources:Utilise our state-of-the-art supercomputing infrastructure to conduct large-scale training and evaluation of generative models.
• Publish and Present Research:Share your findings at top-tier conferences and in leading journals.

Responsibilities:

• Innovate in Deep Learning Research:Develop and implement novel research on multimodal generative foundation models and their biological applications.
• Contribute to Team Success:Collaborate with team members to achieve research goals and contribute to a supportive, knowledge-sharing environment.
• Engage in Collaborative Projects:Initiate and participate in collaborations with external partners and internal departments, including our applied AI team and strategic partners.
• Communicate Findings:Report and present experimental results and research insights both internally and externally, through presentations, written publications, and representing the company at conferences and events.

Requirements:

• Research Experience:Proven experience in professional research settings, either in industry or through academic positions.
• Proven Research Excellence:A strong track record of high-quality research, evidenced by publications in reputable scientific journals or conferences.
• Expertise in Programming:Strong proficiency in software development and programming in Python.
• Experience with Deep Learning Frameworks:Proficiency in frameworks such as PyTorch, JAX, and/or TensorFlow.
• Excellent Communication Skills:Ability to clearly communicate complex ideas, both verbally and in writing, and to work effectively within a multidisciplinary team.
• Strong Analytical and Critical Thinking Skills:Ability to adapt to a dynamic research environment and approach complex challenges creatively.
• Continuous Learning:A demonstrated enthusiasm for staying updated with advancements in AI and contributing novel ideas to the field.

Desired Skills:

• For senior roles, experience mentoring junior team members, leading research projects, and guiding collaborative efforts is a plus.

Knowledge of Machine Learning Domains:

• Generative models leveraging diffusion or Bayesian Flow Networks.
• Modelling multimodal data.
• Large-scale distributed machine learning training.

Knowledge, Experience, or Interest in Biological Domains:

• Drug discovery and protein engineering.
• Understanding of protein language models and structural proteomics.
• Knowledge of genomics.

Research Scientist | Generative Models | Foundational Models | Diffusion | LLMs |Biology