Kenkotech Futures

Job Opportunity

We're partnering with one of the world's leading AI x Biology companies, building frontier foundation models designed to transform therapeutic discovery. The team is developing large-scale multimodal foundation models across biological data modalities - including single-cell genomics, transcriptomics, DNA, RNA and proteins - to create universal biological representations capable of accelerating target discovery, disease understanding and drug development. This is a rare opportunity to join an exceptionally strong research organisation working at the intersection of large-scale machine learning and modern biology. You'll collaborate with world-class AI researchers, computational biologists and experimental scientists to develop the next generation of biological foundation models that directly power therapeutic discovery. The role is highly research-focused, but with a strong emphasis on building models that move beyond publications and have real scientific impact.

Key Responsibilities

• Develop and train large-scale foundation models across biological modalities including DNA, RNA, proteins and single-cell data
• Research novel model architectures, representation learning approaches and pre-training strategies for biological data
• Design and implement large-scale distributed training pipelines for frontier AI models
• Develop methods for multimodal learning across diverse biological datasets
• Improve model performance through post-training, evaluation, alignment and fine-tuning techniques
• Work closely with experimental scientists to translate model outputs into biological insight
• Design rigorous evaluation frameworks for biological foundation models
• Contribute to the long-term research direction of the company's AI platform
• Stay at the forefront of developments across machine learning, foundation models and computational biology

Qualifications

• PhD in Machine Learning, Computer Science, Computational Biology, Bioinformatics, Statistics, Mathematics, Physics or a related quantitative discipline
• Outstanding research background in modern machine learning
• Strong publication record at leading conferences or journals (NeurIPS, ICML, ICLR, Nature, Science, Cell, etc.)
• Experience developing, training or evaluating large deep learning models
• Strong programming skills using modern ML frameworks (PyTorch, JAX, etc.)
• Experience with distributed training or large-scale model development is highly desirable
• Ability to work across both research and engineering to build production-quality systems
• Ideal Background

We're particularly interested in researchers with experience in one or more of the following:

• Foundation Models
• Representation Learning
• Large Language Models
• Multimodal Learning
• Self-Supervised Learning
• Generative Modelling
• Reinforcement Learning
• Large-Scale Distributed Training
• Single-Cell Foundation Models

Why Join

1. Help build some of the world's most advanced foundation models for biology
2. Work alongside internationally recognised AI researchers and computational biologists
3. Apply frontier AI to real therapeutic discovery problems
4. Access enormous proprietary biological datasets and large-scale compute
5. Research with genuine scientific and clinical impact rather than purely academic objectives
6. Join one of the best-capitalised and fastest-growing AI x Biology organisations in the world
7. Opportunity to publish, innovate and help shape the future of AI-driven drug discovery