Job Description

AI / ML Research Scientist | AI for Scientific Discovery (AI x Bio)

Competitive Base + Meaningful Equity

Hybrid (San Francisco)

AI-First Scientific R&D Company

PLEASE FOLLOW THE KENKOTECH PAGE AND CONNECT WITH THE JOB POSTER

We’re working with a well-capitalized, AI-native company building foundation models and learning systems to accelerate breakthroughs across biology and the life sciences.

This team isn’t just applying AI to science - they are rethinking how science itself is done. From training models that can reason over biological systems to developing autonomous workflows that generate and test hypotheses, their work sits at the frontier of AI x Bio.

They are hiring AI / ML Research Scientists to push the boundaries of what machine learning can do in real-world scientific discovery. This is a deeply technical, research-driven role with direct impact on how new therapies, insights, and technologies are created.

What You Will Work On

• Develop novel machine learning approaches for modeling complex biological systems
• Train and adapt large-scale models (e.g. foundation models, multimodal systems) on scientific data
• Design systems that can reason, generalize, and generate testable scientific hypotheses
• Work closely with engineers and domain experts to bring research into real-world workflows
• Explore emerging paradigms (e.g. agentic systems, self-improving models, automated science)

What They’re Looking For

• Strong background in machine learning / AI (PhD or equivalent industry experience)
• Experience developing and training deep learning models from first principles
• Hands-on experience with modern architectures (transformers, diffusion, multimodal models, etc.)
• Strong coding ability (Python, PyTorch and/or JAX)

Bonus Skills

• Experience with biological data (genomics, proteomics, drug discovery, etc.)
• Experience with large-scale model training or distributed systems
• Experience with LLMs, reasoning systems, or agentic workflows
• Background in reinforcement learning or generative modeling
• Track record of research (papers, open-source, or impactful industry work)

Location & Work Environment

This is a hybrid role. While remote candidates in the Eastern Time Zone (U.S.) are welcome, there is a preference for individuals able to periodically collaborate in person with the team.

Interested candidates should apply via direct message or LinkedIn application.

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