Scientist II Machine Learning Engineer
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This role is about turning research code into reliable machine learning systems, with a strong focus on large language models and generative AI. It asks for roughly five years of Python work, real experience putting deep learning models into production, and comfort with PyTorch or TensorFlow, Hugging Face, and cloud tools like SageMaker. A strong candidate also helps scientists write cleaner, testable code and sets up tracking so experiments can be repeated.
How it fits the AI era
The work sits right inside the AI shift, since it covers building agent style workflows, retrieval systems, and faster model serving and training at scale. AI tools can speed up prototyping and code writing, but the judgment about what is reliable, reproducible, and safe for biomedical use stays with people. Coaching researchers and explaining technical tradeoffs to different audiences also stays human.
How it builds AI experience
Working on a product where an AI scientist supports pharmaceutical research gives hands‑on exposure to agents, language models, and the infrastructure behind them, applied to real biology questions. Anyone wanting to build toward this can practice with open models on Hugging Face, build a small retrieval or agent project on public biomedical data, and try distributed training or fast inference tools. Experience with clinical or omics data would add to the fit.
Worth showing
- Deploying deep learning to production
- LLM, RAG, and agent projects
- PyTorch and Hugging Face depth
- Cloud MLOps with SageMaker
- Testing, CI/CD, and Docker habits
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
About Owkin
Per the company, Owkin builds K Pro, an AI scientist that works alongside pharmaceutical research teams to speed discovery and strategic decisions.
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