Scientist II Machine Learning Engineer (LLM & GenAI Specialist)

🏢 Owkin · 📍 Paris · 💼 hybrid · ⏰ FullTime

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Position Summary & Responsibilities

About us

Owkin is an agentic AI company pioneering Biological Artificial Superintelligence to solve problems in biology where human researchers alone have failed.
Owkin builds K Pro - an AI scientist for pharmaceutical research and strategic decision-making. K Pro orchestrates a suite of AI skills and tools to decode complex biology, accelerate research, and dramatically increase productivity.
K Pro is built on Owkin’s unrivalled multimodal patient data network, state-of-the-art AI for biology and a decade of experience working with pharmaceutical partners.

Position is based in our Paris offices or remotely in France, UK, or Germany.

Please submit your CV in English

About the role:

We are seeking a highly skilled and experienced Scientist II Machine Learning Engineer to design, develop, and deploy cutting-edge AI solutions. In this role, you will focus heavily on Large Language Models (LLMs), Generative AI, and Advanced Deep Learning architectures.

You will bridge the gap between experimental data science and production-ready ML systems. Working closely with our Data Science and Engineering teams, you will scale complex models, optimize training and inference pipelines, and leverage cloud ecosystems to deliver robust biomedical solutions. Experience handling health, clinical, or omics data (genomics, transcriptomics, proteomics, etc.) is a major plus.

In particular, you will:

Develop scalable machine learning libraries, tooling, and research workflows in close partnership with research and data scientists.
Shape research infrastructure requirements and drive tool and software choices in collaboration with the platform team.
Champion software engineering best practices across the team, enabling researchers and scientists to write maintainable, testable, efficient, and scalable code.
Design and enhance systems for experiment tracking, reproducibility, and end-to-end model lifecycle management.
Contribute to and accelerate t

🎯 Candidate Preparation Guide For Owkin Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Tests cutting-edge machine learning capabilities in federated learning applied to biology, medicine, and oncology research.

📋 Preparation Priorities (Key Areas to Clarify):

1. Proposing black-box models for medical diagnostics without interpretability or explainability
2. Lack of understanding regarding clinical data governance, GDPR, and HIPAA data silos
3. Treating biomedical data engineering as standard tabular or web data science

✨ Recommended Role Alignment Hook:

I build robust, privacy-preserving machine learning architectures that unlock insights across fragmented biomedical datasets without compromising data governance.

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