• Build reliable, efficient, and scalable infrastructure for our AI/ML capabilities • Create robust data pipelines to feed analyses and models • Enable forecasting, network orchestration, and live pricing systems • Ensure data quality and data integrity through best practices in data integration • Build out robust feature stores, model orchestration tooling, experimentation tooling, model performance monitoring • Create standards and templates for model development and deployment across all Data Science teams

• Partner with PhD researchers to design, implement, and productize machine learning models that drive quantitative trading strategies • Develop and maintain complex data pipelines, including data ingestion, feature engineering, validation, and quality monitoring • Translate research prototypes and novel ideas into performant, well-tested, production-ready code • Build extensible tools and frameworks that accelerate the model development and experimentation lifecycle • Supervise, understand, and remediate subtle data quality issues across both research and production environments • Proactively lead projects from requirements through delivery, making autonomous decisions about scope, dependencies, and trade-offs, with an emphasis on long-term maintainability • Coordinate and contribute to deployment efforts while guiding junior engineers and researchers; align with research and engineering stakeholders on ownership, execution, and prioritization • Foster engineering consistency, standards, and best practices within Research

• Lead the development, testing, and deployment of ML models and pipelines • Design and refine GenAI/LLM-based models to streamline and automate clinical trial operations • Partner with clinicians, informaticists, data scientists, and engineers to build solutions aligned with Paradigm’s mission and goals • Drive improvements in model deployment infrastructure, develop monitoring tools, and enhance model performance • Mentor junior ML engineers and contribute to team knowledge-sharing

• Own the end-to-end ML lifecycle and deployment — experiment tracking, model registry, versioning, lineage, and reproducibility (e.g., MLflow, Weights & Biases, Kubeflow); • Design and operate model serving for batch and low-latency online inference with autoscaling, GPU efficiency, and performance optimization (batching, quantization, caching); • Partner with bioinformatics and computational biology teams to productionize large-scale protein design and structure-prediction experiments; • Implement CI/CD, continuous training, and observability for ML; • Drive GPU and accelerated-compute efficiency; • Build self-service ML tooling and provide technical leadership.