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<p><strong>About QuantHealth<br></strong><br>QuantHealth is a fast-growing AI company transforming drug development through clinical simulations, predictions of disease progression and treatment effects, and large-scale biomedical AI. <br>Our platform combines real-world patient data from over 350 million patients, biomedical knowledge graphs, and advanced machine learning models to simulate clinical trials and predict<br>patient outcomes. Pharmaceutical companies use QuantHealth platform to optimize trial design, reduce development risk, and accelerate the development of new therapies.<br>At the core of our platform is a family of proprietary AI models that learn from large-scale longitudinal healthcare data and biomedical knowledge to model disease progression, treatment effects, and clinical trial outcomes.<br><br><strong>About the Role<br></strong><br>We are looking for a Senior AI Research Engineer to help develop the next generation of QuantHealth’s core AI technology.<br>This is a highly technical, hands-on role focused on advancing our foundation models and predictive modeling capabilities. You will work closely with the Director of AI & Algorithms<br>and a team of researchers and engineers to develop novel machine learning approaches that improve how clinical outcomes, treatment effects, and patient trajectories are modeled.<br>The ideal candidate combines strong research instincts with exceptional implementation skills. You are comfortable reading and evaluating cutting-edge research, designing new modeling<br>approaches, and turning ideas into robust, production-ready systems. This role is primarily an individual contributor position, with a strong emphasis on research,<br>experimentation, algorithm development, and technical execution.<br><br><strong>Responsibilities</strong></p> <ul> <li>Design, develop, and evaluate novel machine learning algorithms that advance<br>QuantHealth’s core modeling capabilities.</li> <li>Drive the development of significant components of the next generation of QuantHealth<br>foundation models and predictive modeling systems.</li> <li>Evaluate, implement, and extend state-of-the-art machine learning research and translate<br>promising advances into QuantHealth’s modeling platform.</li> <li>Research and implement state-of-the-art approaches in areas such as: <ul> <li>Transformer architectures</li> <li>Self-supervised and representation learning</li> <li>Foundation models</li> <li>Multimodal learning</li> <li>Knowledge-graph-enhanced modeling</li> <li>Temporal modeling of longitudinal patient data</li> <li>Causal and treatment-effect modeling</li> <li>Uncertainty quantification</li> </ul> </li> <li>Design and evaluate new pre-training objectives, model architectures, representations,<br>and learning strategies.</li> <li>Develop rigorous validation methodologies and contribute to benchmarking and<br>evaluation frameworks.</li> <li>Implement research ideas efficiently and at high-quality using modern machine learning<br>frameworks.</li> <li>Collaborate closely with Clinical Teams, DataOps, MLOps, Product, and Engineering<br>teams.</li> <li>Stay current with advances in machine learning and identify opportunities to incorporate<br>relevant innovations into QuantHealth’s platform.</li> <li>Communicate technical findings clearly and proactively raise risks, limitations, and<br>opportunities when identified.</li> <li>Contribute to scientific publications, patents, and external thought leadership initiatives<br>when appropriate.<br><br><strong>Qualifications<br></strong></li> <li>MSc or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Physics,<br>Computational Biology, or a related quantitative discipline. PhD strongly preferred.</li> <li>5+ years of experience developing advanced machine learning systems in industry,<br>academia, or both.</li> <li>Strong hands-on experience developing deep learning systems using PyTorch or<br>equivalent frameworks.</li> <li>Demonstrated experience designing, implementing, and evaluating novel machine<br>learning approaches.</li> <li>Deep expertise in modern machine learning architectures, including transformer-based<br>models, self-supervised learning, representation learning, and foundation models.</li> <li>Experience designing, training, adapting, and optimizing transformer-based and other<br>large-scale machine learning models, including distributed training and large-scale<br>experimentation environments.</li> <li>Strong background in machine learning, statistics, optimization, and experimental design.</li> <li>Experience translating research concepts into reliable software and production-ready<br>systems.</li> <li>Excellent software engineering skills and coding practices.</li> <li>Strong communication skills and ability to work effectively in highly cross-functional<br>environments.</li> <li>Proven ability to work independently, drive complex projects, and operate with high<br>ownership.</li> <li>Proven ability to critically evaluate scientific literature and independently identify<br>promising research directions.<br><br><strong>Strong Advantages<br><br></strong></li> <li>Experience developing foundation models, large language models, or large-scale self-<br>supervised learning systems.</li> <li>Experience with multimodal machine learning.</li> <li>Experience with graph neural networks, knowledge graphs, or representation learning<br>over structured biomedical data.</li> <li>Experience with causal inference, treatment-effect estimation, survival analysis, or time-<br>to-event modeling.</li> <li>Experience working with healthcare, biomedical, pharmaceutical, or real-world patient<br>data.</li> <li>Track record of publications at leading machine learning or AI conferences.</li> <li>Experience working in high-growth startup environments.</li> <li>Experience implementing and extending state-of-the-art research papers.</li> </ul>