• 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.

• Work closely with the Data Science team to develop and implement ML algorithms into production code. • Apply design and coding best practices, including vectorization and object-oriented programming in Python. • Ensure algorithms cover different scenarios with a focus on performance and scalability. • Rapid ramp-up to work on deliverables within Scrum teams. • Collaborate with cross-functional teams to integrate ML solutions into production systems. • Utilize CI/CD, version control (Git), and automated testing in ML projects.

• Play a critical role in building SOTA LLMs and foundation models within a small, high-impact team • Help build a reliable, maintainable and scalable software stack • Produce world-leading models that are open-sourced and integrated into new Flower Lab products • Design, implement and optimize core components across the full spectrum of stages relevant to frontier model building: data curation, evals, pre-training, post-training • Diagnose and resolve GPU/kernel issues, memory/storage bottlenecks, and multi-node failures at scale • Collaborate on the debugging of training instabilities and related issues • Devise surrounding infrastructure, tooling, monitoring, and observability, essential for large-scale LLM development • Contribute ideas, be heard and influence the direction of the company across the board.

• Implement a proof of concept of adversarial attacks on malware recognition systems • Develop an automated generator of executables that are classified as "benign"