• Provide technical leadership at the intersection of biomedical foundation models, computational biology, and clinical translation. • Shape how genomic, pathology and multimodal foundation models are applied to high-impact translational problems. • Operate with broad technical autonomy, influencing modeling strategy across multiple initiatives. • Work closely with AI scientists, translational scientists, bioinformatics, clinical partners, and ML engineers to ensure foundation models deliver biologically grounded and clinically meaningful insights.

• Migrate box and barcode detection pipelines to cloud infrastructure following MLOps best practices • Build and maintain CI/CD pipelines for deployment across production and non-production environments • Implement automated rollback, canary, and blue-green deployment strategies for ML microservices • Build out a multi-tenant MLOps platform using tools like Prefect, ZenML, or similar orchestration frameworks • Establish a centralized model registry and versioning system for all production assets • Instrument observability across the ML stack — logging, metrics, and distributed tracing — to ensure reliability at scale

• Design, build, and deploy production-grade machine learning models and systems at scale • Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring • Build scalable data and model pipelines with strong reliability, observability, and automated retraining • Work with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems. • Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions • Improve system performance across latency, throughput, and model quality metrics • Research and apply state-of-the-art machine learning and AI techniques, including deep learning, graph & transformers based, and LLM evaluation/alignment • Contribute to technical strategy, architecture, and long-term ML roadmap

• Translating Requirements: Interpret vague requirements and develop models to solve real-world problems. • Data Science: Conduct ML experiments using programming languages with machine learning libraries. • GenAI: Leverage generative AI to develop innovative solutions. • Optimisation: Optimise machine learning solutions for performance and scalability. • Custom Code: Implement tailored machine learning code to meet specific needs. • Data Engineering: Ensure efficient data flow between databases and backend systems. • MLOps: Automate ML workflows, focusing on testing, reproducibility, and feature/metadata storage. • ML Architecture Design: Create machine learning architectures using Google Cloud tools and services. • Engineering Software for Production: Build and deploy production-grade software for machine learning and data-driven solutions.