AI Scientist

KenkoTech Futures; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

KenkoTech Futures is seeking a highly trained PhD researcher to develop and train large-scale foundation models that span DNA, RNA, proteins and single-cell data. You will work with AI researchers, computational biologists and experimental scientists to push the boundaries of therapeutic discovery.

The role emphasizes research impact beyond publications, with production-ready model development and collaboration across disciplines in a fast-growing AI-biotech setting.

Qualifications

• PhD in Machine Learning, Computer Science, Computational Biology, or related field.
• Outstanding research record with publications at top venues.
• Experience with distributed training and large-scale models.
• Ability to bridge research and engineering to deploy production systems.
• Familiarity with self-supervised learning is a plus.

Responsibilities

• Develop and train large-scale foundation models across biological modalities.
• Research architectures and pre-training strategies for biological data.
• Design and implement large-scale distributed training pipelines.
• Develop multimodal learning methods across diverse datasets.
• Collaborate with scientists to translate outputs into biological insights.
• Create rigorous evaluation frameworks for foundation models.
• Contribute to long-term research direction of the AI platform.
• Stay at the frontier of ML, foundation models, and computational biology.

Skills

Strong ML background

Distributed training

Cross-disciplinary collaboration

Production-quality systems

Self-supervised learning

Education

PhD in ML/CS/Computational Biology

Tools

PyTorch

TensorFlow

Distributed training frameworks

Job description

About: Frontier AI x Biology | Foundation Models | Therapeutic Discovery

Stage: Well-funded AI Biotechnology Company

PLEASE FOLLOW THE KENKOTECH PAGE AND CONNECT WITH THE JOB POSTER

About the 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 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:

• Self-Supervised Learning

Why Join

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