Top AI & Machine Learning Jobs

Senior ML engineer building and deploying deep learning models (transformers, foundation models, transfer learning) for oncology real-world data. Responsibilities include research and development of predictive models (digital twins, endpoint prediction, trial optimization), extending models across data sources, supporting client engagements, and partnering with product and engineering to productionize reproducible, maintainable solutions while communicating results to technical and non-technical stakeholders.

Maintain operational tools and metrics to support real-world evidence deliveries; transform data into actionable insights using statistical methods and visualizations; build and support dashboards and data tools; run ad-hoc analyses, experiments, and predictive models; collaborate with cross-functional stakeholders and operations teams to prioritize and improve processes.

Build and maintain evaluation frameworks, observability, and reliability practices for AI/LLM-powered workflows. Define metrics (SLOs/SLIs), monitor drift and hallucination risk, design guardrails for multi-agent systems, support model selection and platform integration, and collaborate across product, security, and data science teams while participating in on-call rotations.

Design and build evaluation, observability, and reliability infrastructure for AI/LLM/agent workflows. Define SLOs/SLIs, monitoring, drift detection, testing pipelines, and guardrails. Partner across platform, product, security, and data science teams to enable safe, scalable AI adoption and participate in on-call rotations.

Lead a Germany-based Global Platform Enablement team to evolve platform capabilities for AI enablement and global support. Drive technical direction for reusable AI platform features, architecture, observability, governance, and developer experience while partnering with cross-functional and US-based platform leaders. Build team capability, guide architectural tradeoffs across automation and agentic systems, and ensure operational excellence and alignment with regulatory and regional requirements.

Apply and validate ML and LLM systems to extract clinical information from unstructured medical notes, develop and productionize ML-derived datasets, collaborate with clinicians and engineers, perform dataset performance analyses, and provide analytic support to cross-functional stakeholders.

Design, evaluate, and productionize LLM-based AI products for oncology data. Create custom evaluations, analyze model outputs, collaborate with clinicians and engineers, and support product improvement, monitoring, and stakeholder communication.