At Databricks, I orchestrated a transition from monolithic training pipelines to a distributed Ray architecture that reduced total model training time by forty percent for our core large language models. This move addressed the scaling limitations we faced as our dataset volume surged beyond five terabytes per day, requiring a more robust orchestration layer than our legacy Spark jobs could provide. Bringing this expertise in high throughput infrastructure to Scale AI as a Senior ML Infrastructure Engineer offers an immediate opportunity to improve the training efficiency of your data labeling pipelines.
Building on this, I optimized our Kafka and Airflow pipelines at Waymo to process sensor data streams with sub-millisecond latency, ultimately increasing overall system ingestion throughput by sixty percent. I later utilized JAX and PyTorch in my current role at Databricks to implement a model monitoring framework that tracked drift across five hundred concurrent models, significantly reducing manual intervention requirements. Furthermore, I migrated our Hugging Face library integrations to a specialized containerized environment, which improved developer deployment frequency by thirty percent while maintaining strict internal data governance standards across production clusters.
Scale AI plays a critical role in the AI ecosystem by accelerating the development of foundation models through high quality data annotation. My background in building distributed infrastructure and managing large scale data ingestion pipelines aligns directly with your need to maintain the world leading performance of your labeling services. By leveraging my deep understanding of the Ray framework for distributed computing and my ability to optimize data pipelines through Spark, I can help scale your infrastructure to meet the increasing demand for high fidelity model training data.
Delivering robust infrastructure that handles massive data workloads is a primary focus of my engineering practice. Bringing my technical experience from Waymo and Databricks to your team will facilitate faster model iterations and improved system stability for your customers. I look forward to discussing how my work with distributed systems can contribute to the engineering challenges currently facing your infrastructure team. Please review my attached resume for further details on these projects, and I welcome an interview to explore how my experience solves your current infrastructure bottlenecks.
Best regards,
Amara Diallo
