• Join as one of the founding members of the Flower Frontier Model Team, a new group at Flower Labs charged with building category-defining models. • Build SOTA LLMs and foundation models within a small, high-impact team. • Design, implement and optimize core components across the full spectrum of stages relevant to frontier model building: data curation, evals, pre-training, post-training. • Collaborate on the debugging of training instabilities and related issues. • Devise surrounding infrastructure, tooling, monitoring, and observability for large-scale LLM development.

• Build and deploy AI-driven products that accelerate clinical trials and improve patient outcomes. • Develop advanced ML models and LLM-powered agents for critical use cases like patient recruitment. • Leverage modern cloud tools and MLOps best practices to build robust data pipelines. • Collaborate across teams of data scientists, product managers, and engineers to integrate AI capabilities. • Stay up-to-date with the latest developments in ML/AI and proactively bring new ideas to the team.

• Utilize in-house and 3rd party LLMs to solve language understanding problems • Employ techniques such as fine-tuning and RAG to improve models • Contribute to designing, building, evaluating, shipping, and refining Spotify’s product by hands-on ML development • Help drive optimization, testing, and tooling to improve quality of our content enrichment assets • Collaborate with cross-functional teams of MLEs, data and backend engineers, and other stakeholders including tech research, data science, and product to develop new features and technologies • Be a participant in our AI Foundation’s ML community and work collaboratively and efficiently within our existing platforms and systems • Perform data analysis to establish baselines and inform product decisions • Stay up-to-date on the latest machine learning algorithms and techniques

• Lead the algorithm selection, design, and prototyping of machine learning models to solve complex business problems, including recommendation, personalization, and predictive analytics. • Apply your expertise in statistical modeling and machine learning to perform deep data analysis, guide crucial feature selection, and identify opportunities for product improvement. • Own the full ML lifecycle, from breaking down discrete steps of a pipeline (e.g., with a DAG) to analyzing model implementations and improving their robustness in the wild. • Implement and manage robust model observability, tuning, and optimization processes to ensure sustained performance and accuracy post-deployment. • Develop and maintain data pipelines to process and prepare data for model training and evaluation. • Design and conduct A/B tests to evaluate model performance and its impact on key business metrics. • Collaborate closely with product managers and engineers to define problems and deliver effective AI-driven solutions. • Mentor other team members, champion best practices in machine learning engineering, and stay current with the latest advancements in the field.