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Owkin is an agentic AI company building K Pro, an AI scientist for pharmaceutical research. K Pro orchestrates AI skills and tools to decode complex biology, accelerate research, and increase productivity for pharmaceutical partners. The company leverages a multimodal patient data network, state-of-the-art AI for biology, and a decade of pharma partnership experience.
As a Scientist II Machine Learning Engineer, you will design, develop, and deploy cutting-edge AI solutions with a heavy focus on Large Language Models (LLMs), Generative AI, and advanced deep learning architectures. You will bridge experimental data science and production-ready ML systems, working closely with Data Science and Engineering teams to scale complex models, optimize training and inference pipelines, and deliver robust biomedical solutions.
Key responsibilities include:
- Develop scalable machine learning libraries, tooling, and research workflows in partnership with research and data scientists
- Shape research infrastructure requirements and drive tool and software choices with the platform team
- Champion software engineering best practices, enabling researchers 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 rapid prototyping of novel models, bridging early research and robust implementation
- Ad-hoc responsibilities and projects as assigned
The role is based in Paris offices or remote within France, UK, or Germany.
REQUIREMENTS:
- Education: Master's or Ph.D. in Computer Science, Data Science, or related quantitative field with strong ML/AI focus
- Experience: 5+ years professional software development with Python and proven track record deploying deep learning models to production
- ML/DL Frameworks: Deep understanding of machine learning algorithms, statistical methods, and deep learning frameworks; mastery of PyTorch or TensorFlow
- LLM Specialization: Deep understanding of Transformer architectures, attention mechanisms, and latest Generative AI breakthroughs; extensive Hugging Face experience with knowledge of agentic workflows and RAG
- Cloud & MLOps: Experience with AWS, GCP, or Azure and SageMaker
- Software Engineering: Strong practices in version control, comprehensive testing, CI/CD, and containerization (Docker)
- Workflow Optimization: Experience with distributed computing, distributed training strategies for massive datasets, and ML workflow optimization (e.g., vLLM)
PREFERRED:
- Domain expertise: Experience with clinical or omics data (genomic sequencing, transcriptomics, electronic health records) and medical imaging
- Open-source ML contributions or research publications
- Soft skills: Excellent problem-solving, strong communication, mentorship capability, proactive debugging of distributed systems, autonomy in task execution
