• Architect the ML Ecosystem: You will own the end-to-end lifecycle of our ML infrastructure, designing a scalable, modern environment that enables models to thrive in production. • Productionize Innovation: Partner closely with our Data Science team to take complex algorithms from the 'lab' to the 'real-world', building the high-performance pipelines required to scale them. • Engineer Feature Intelligence: Design and maintain both offline and online feature stores, ensuring our models have the high-quality data they need for instant decision-making. • Escale the Data Platform: Collaborate with Data Engineers to evolve our data lake and ETL architectures, ensuring our data platform remains robust and future-proof. • Ensure System Health: Lead the monitoring and observability strategy for all production models, ensuring reliability and performance through proactive maintenance. • Shape Technical Strategy: Act as a key stakeholder in architectural decisions, helping the broader team define the strategy for our data products and event-driven architectures.

• 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