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Principal Machine Learning Research Scientist
Sin especificarTiempo completoArtificial Intelligence ResearchPharmaceuticals
Advance applied machine learning research for pharmaceutical R&D, developing novel methods for scientific and decision-making workflows. Collaborate across research and engineering teams to validate methods and translate promising findings into reference implementations.
Responsabilidades
- Conduct applied machine learning research beyond standard off-the-shelf approaches for pharmaceutical R&D workflows.
- Investigate emerging methods, identify limitations in current AI approaches, and develop new methods for scientific and decision-making challenges.
- Translate research findings into reference implementations and demonstrable capabilities for priority R&D use cases.
- Validate methods and connect methodological innovation to practical scientific problems.
- Develop research collaborations with academic institutions and external scientific partners.
- Contribute to scientific publications, conference presentations, and other research outputs.
- Evaluate new research, reproduce relevant advances, and identify opportunities for measurable impact.
Requisitos
- PhD in machine learning, artificial intelligence, computer science, statistics, applied mathematics, biomedical engineering, or a related quantitative discipline.
- At least 5 years of professional or research experience in machine learning, with a strong publication record, including contributions to leading conferences or comparable venues.
- Deep expertise in one or more areas such as foundation models, LLM reasoning, multimodal learning, causal machine learning, digital twins, probabilistic modelling, uncertainty quantification, or sequential decision-making.
- Experience developing, implementing, and rigorously evaluating novel machine learning methods.
- Proficiency in Python and modern machine learning frameworks such as PyTorch, JAX, or TensorFlow.
- Experience designing reproducible experiments, establishing baselines, conducting ablation studies, and evaluating methods with rigorous benchmarks.
- Ability to formulate research questions relevant to real-world scientific problems and assess limitations of current methods.
- Experience translating research prototypes into reference implementations for applied AI or software engineering teams.
- Comfort working with complex, heterogeneous data, including clinical, molecular, imaging, real-world, digital health, or scientific data.
- Experience contributing to collaborations with academic researchers, industry scientists, and multidisciplinary teams.
- Strong scientific writing and communication skills.
Se valora
- Experience in pharmaceutical R&D, healthcare, life sciences, or another regulated scientific environment.
- Patents, open-source software, or other high-impact scientific outputs.