Senior Machine Learning Engineer, Predictive Modeling & Applied AI
About The Position
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? 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.
Requirements
- 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
- At least 5 years of experience building and shipping deep learning models in industry or research
- Fluent in modern deep learning methods, including transformer architectures, foundation models, transfer learning, and neural networks for multimodal or longitudinal data
- Proficient in Python and a deep learning framework such as PyTorch or TensorFlow
- 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
- Experience taking models from research into production and care about reproducibility, evaluation, and maintainability
- Comfortable operating in a matrixed, fast-paced environment and balancing multiple high-priority initiatives
- Can translate technical concepts into clear, decision-relevant explanations for technical and non-technical stakeholders
Nice To Haves
- 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
- Have built digital twins, clinical trial simulations, or other patient-level simulation models
- Experience with causal inference or with statistical methods for longitudinal and time-to-event data
- Worked with multimodal data such as clinical text, imaging, and structured clinical data, or have experience with LLMs for clinical NLP
- Deployed models in regulated or healthcare decision-making settings
- Contributed to publications, technical blog posts, or other external communications
Responsibilities
- 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
Benefits
- Flexible work hours
- Flexible paid time off
- Comprehensive compensation package
- 401(k) contribution
- Financial health resources including 1:1 financial advice
- Mental well-being tools and services
- Parental benefits and policies including family-building care and generous leave
- Path to parenthood programs supporting fertility, adoption and surrogacy
- Travel support for safe healthcare services
- Continuous learning support
- Inclusion and belonging support
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What This Job Offers
Job Type
Full-time
Career Level
Senior
