Job Opportunities API

Reimagine the infrastructure of cancer care within a community that values integrity, inspires growth, and is uniquely positioned to create a more modern, connected oncology ecosystem. We're looking for a Senior Machine Learning Engineer to help us accomplish our mission to improve and extend lives by learning from the experience of every person with cancer. Are you ready to be the next changemaker in cancer care? What You'll Do In this role, you will work as a senior machine learning engineer within our Product Data Science organization, supporting the Scientific Engagement and Applied Research (SEAR) team. You will build deep learning models that turn oncology real-world data into decision-grade tools for pharmaceutical and academic partners. You will research and develop novel modeling approaches for hard problems, and ship solutions that support both our research agenda and specific client projects.  In addition, you will: Build, train, and validate deep learning models for oncology real-world data, including transformer architectures, foundation models, and transfer learning approaches Develop predictive models for use cases such as digital twins, endpoint prediction, trial optimization, and treatment effect estimation Apply transfer learning and domain adaptation to extend models across data sources (for example EHR, claims, and multimodal data) and across oncology indications Support services and client engagements that require deep learning, building predictive models for specific partner use cases Partner with product, engineering, and data teams to shape novel capabilities into scalable solutions across our organization Write clear documentation and explain model design, behavior, and limitations to both technical and non-technical partners Stay current with deep learning methods and bring promising approaches into our work Who You Are You're a kind, passionate and collaborative problem-solver who values the opportunity to think beyond the way things are. You are a strong machine learning engineer who likes exploring novel approaches and pushing the boundaries of what is possible to achieve with data.  You are motivated by end use cases of the models you build, and not just abstract performance metrics.  You are comfortable owning technical work end to end, from prototype to production, and you communicate clearly with people who do not share your background. You have an advanced degree (MS, PhD, or equivalent experience) in a quantitative or technical field (for example computer science, machine learning, applied mathematics, statistics, or physics), or demonstrated equivalent expertise through applied work in industry You have at least 5 years of experience building and shipping deep learning models in industry or research You are fluent in modern deep learning methods, including transformer architectures, foundation models, transfer learning, and neural networks for multimodal or longitudinal data You are proficient in Python and a deep learning framework such as PyTorch or TensorFlow You have experience working with large-scale, longitudinal datasets, ideally in healthcare (for example EHR, claims, or multimodal clinical data), or you can ramp quickly on data of that kind You have experience taking models from research into production and care about reproducibility, evaluation, and maintainability You are comfortable operating in a matrixed, fast-paced environment and balancing multiple high-priority initiatives You can translate technical concepts into clear, decision-relevant explanations for technical and non-technical stakeholders Extra credit You have experience with oncology or other clinical real-world data, and familiarity with the variables, endpoints, and study designs commonly used in oncology RWE research and observational studies You have built digital twins, clinical trial simulations, or other patient-level simulation models