Senior Machine Learning Engineer, Predictive Modeling & Applied AI

About the role

We are seeking a Senior Machine Learning Engineer to join our Product Data Science organization and support the Scientific Engagement and Applied Research (SEAR) team. Your role will involve building deep learning models that transform oncology real-world data into actionable tools for pharmaceutical and academic partners. You will also work on developing novel modeling approaches and shipping solutions that align with our research agenda and specific client projects.

Responsibilities

• Build, train, and validate deep learning models for oncology real-world data, including transformer architectures, foundation models, and transfer learning approaches.
• Research and develop novel modeling approaches for complex problems, and ship solutions that support both our research agenda and specific client projects.
• 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.

Requirements

• You are 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.

Qualifications

• 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.
• Experience with 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.
• Experience with multimodal data such as clinical text, imaging, and structured clinical data, or experience with LLMs for clinical NLP.
• Experience with deploying models in regulated or healthcare decision-making settings.
• Contributions to publications, technical blog posts, or other external communications.

Where You’ll Work

This hybrid role includes work from home and 3 office days set by you and your team. For more information on our approach to hybrid work, please visit the how we work website.

Benefits

• Comprehensive compensation package
• 401(k) contribution
• Mental well-being tools and services
• Parental benefits and policies
• Travel support for safe healthcare services

Salary Range

$163,200 - $224,400