Q
Senior AI Research Engineer
Не указаноПолная занятостьAI & Machine LearningHealthcare Technology
Develop research-driven machine learning models and predictive systems for clinical outcomes, treatment effects, and patient trajectories. Turn advanced research into robust, production-ready technology in collaboration with research, clinical, data, product, and engineering teams.
Обязанности
- Design, develop, and evaluate machine learning algorithms and predictive models.
- Help build foundation models and modeling systems for clinical outcomes, treatment effects, and patient trajectories.
- Assess and implement current research, translating promising advances into production-ready systems.
- Research and apply methods including transformers, self-supervised and representation learning, multimodal learning, knowledge-graph-enhanced modeling, temporal modeling, causal modeling, and uncertainty quantification.
- Develop and assess pre-training objectives, architectures, representations, and learning strategies.
- Create validation methods and contribute to benchmarking and evaluation frameworks.
- Implement research efficiently using modern machine learning frameworks.
- Collaborate with clinical, DataOps, MLOps, product, and engineering teams; share findings and surface risks and opportunities.
- Contribute to scientific publications, patents, and external thought leadership when appropriate.
Требования
- MSc or PhD in computer science, machine learning, statistics, mathematics, physics, computational biology, or a related quantitative field; a PhD is preferred.
- At least 5 years developing advanced machine learning systems in industry or academia.
- Hands-on experience with deep learning frameworks such as PyTorch.
- Demonstrated ability to design, implement, and evaluate novel machine learning approaches.
- Strong knowledge of transformers, self-supervised and representation learning, and foundation models.
- Experience training, adapting, and optimizing large-scale models, including distributed training and large-scale experimentation.
- Strong grounding in machine learning, statistics, optimization, and experimental design.
- Ability to translate research into reliable software and production-ready systems, with strong software engineering practices.
- Ability to evaluate scientific literature, identify research directions, work independently, and communicate effectively across teams.
Будет плюсом
- Experience with foundation models, large language models, or large-scale self-supervised learning.
- Experience in multimodal machine learning, graph neural networks, knowledge graphs, or structured biomedical data.
- Experience with causal inference, treatment-effect estimation, survival analysis, or time-to-event modeling.
- Experience with healthcare, biomedical, pharmaceutical, or real-world patient data.
- Publications at leading machine learning or AI conferences; startup experience; or experience extending research papers.