Senior AI Research Engineer - QuantHealth

MSc or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Computational Biology, or a related quantitative discipline. PhD strongly preferred. 5+ years of experience developing advanced machine learning systems in industry, academia, or both. Strong hands-on experience developing deep learning systems using PyTorch or equivalent frameworks. Demonstrated experience designing, implementing, and evaluating novel machine learning approaches. Deep expertise in modern machine learning architectures, including transformer-based models, self-supervised learning, representation learning, and foundation models. Experience designing, training, adapting, and optimizing transformer-based and other large-scale machine learning models, including distributed training and large-scale experimentation environments. Strong background in machine learning, statistics, optimization, and experimental design. Experience translating research concepts into reliable software and production-ready systems. Excellent software engineering skills and coding practices. Strong communication skills and ability to work effectively in highly cross-functional environments. Proven ability to work independently, drive complex projects, and operate with high ownership. Proven ability to critically evaluate scientific literature and independently identify promising research directions. Strong Advantages Experience developing foundation models, large language models, or large-scale self- supervised learning systems. Experience with multimodal machine learning. Experience with graph neural networks, knowledge graphs, or representation learning over structured biomedical data. Experience with causal inference, treatment-effect estimation, survival analysis, or time- to-event modeling. Experience working with healthcare, biomedical, pharmaceutical, or real-world patient data. Track record of publications at leading machine learning or AI conferences. Experience working in high-growth startup environments. Experience implementing and extending state-of-the-art research papers.

Design, develop, and evaluate novel machine learning algorithms that advance QuantHealth’s core modeling capabilities. Drive the development of significant components of the next generation of QuantHealth foundation models and predictive modeling systems. Evaluate, implement, and extend state-of-the-art machine learning research and translate promising advances into QuantHealth’s modeling platform. Research and implement state-of-the-art approaches in areas such as: Transformer architectures Self-supervised and representation learning Foundation models Multimodal learning Knowledge-graph-enhanced modeling Temporal modeling of longitudinal patient data Causal and treatment-effect modeling Uncertainty quantification Transformer architectures Self-supervised and representation learning Foundation models Multimodal learning Knowledge-graph-enhanced modeling Temporal modeling of longitudinal patient data Causal and treatment-effect modeling Uncertainty quantification Design and evaluate new pre-training objectives, model architectures, representations, and learning strategies. Develop rigorous validation methodologies and contribute to benchmarking and evaluation frameworks. Implement research ideas efficiently and at high-quality using modern machine learning frameworks. Collaborate closely with Clinical Teams, DataOps, MLOps, Product, and Engineering teams. Stay current with advances in machine learning and identify opportunities to incorporate relevant innovations into QuantHealth’s platform. Communicate technical findings clearly and proactively raise risks, limitations, and opportunities when identified. Contribute to scientific publications, patents, and external thought leadership initiatives when appropriate. Qualifications MSc or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Computational Biology, or a related quantitative discipline. PhD strongly preferred. 5+ years of experience developing advanced machine learning systems in industry, academia, or both. Strong hands-on experience developing deep learning systems using PyTorch or equivalent frameworks. Demonstrated experience designing, implementing, and evaluating novel machine learning approaches. Deep expertise in modern machine learning architectures, including transformer-based models, self-supervised learning, representation learning, and foundation models. Experience designing, training, adapting, and optimizing transformer-based and other large-scale machine learning models, including distributed training and large-scale experimentation environments. Strong background in machine learning, statistics, optimization, and experimental design. Experience translating research concepts into reliable software and production-ready systems. Excellent software engineering skills and coding practices. Strong communication skills and ability to work effectively in highly cross-functional environments. Proven ability to work independently, drive complex projects, and operate with high ownership. Proven ability to critically evaluate scientific literature and independently identify promising research directions. Strong Advantages Experience developing foundation models, large language models, or large-scale self- supervised learning systems. Experience with multimodal machine learning. Experience with graph neural networks, knowledge graphs, or representation learning over structured biomedical data. Experience with causal inference, treatment-effect estimation, survival analysis, or time- to-event modeling. Experience working with healthcare, biomedical, pharmaceutical, or real-world patient data. Track record of publications at leading machine learning or AI conferences. Experience working in high-growth startup environments. Experience implementing and extending state-of-the-art research papers.

MSc or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Computational Biology, or a related quantitative discipline. PhD strongly preferred. 5+ years of experience developing advanced machine learning systems in industry, academia, or both. Strong hands-on experience developing deep learning systems using PyTorch or equivalent frameworks. Demonstrated experience designing, implementing, and evaluating novel machine learning approaches. Deep expertise in modern machine learning architectures, including transformer-based models, self-supervised learning, representation learning, and foundation models. Experience designing, training, adapting, and optimizing transformer-based and other large-scale machine learning models, including distributed training and large-scale experimentation environments. Strong background in machine learning, statistics, optimization, and experimental design. Experience translating research concepts into reliable software and production-ready systems. Excellent software engineering skills and coding practices. Strong communication skills and ability to work effectively in highly cross-functional environments. Proven ability to work independently, drive complex projects, and operate with high ownership. Proven ability to critically evaluate scientific literature and independently identify promising research directions. Strong Advantages Experience developing foundation models, large language models, or large-scale self- supervised learning systems. Experience with multimodal machine learning. Experience with graph neural networks, knowledge graphs, or representation learning over structured biomedical data. Experience with causal inference, treatment-effect estimation, survival analysis, or time- to-event modeling. Experience working with healthcare, biomedical, pharmaceutical, or real-world patient data. Track record of publications at leading machine learning or AI conferences. Experience working in high-growth startup environments. Experience implementing and extending state-of-the-art research papers.