Helm.ai

Architect and design scalable ML infrastructure and services to handle large-scale training and massive datasets. Lead the end-to-end platform lifecycle while mentoring engineers and collaborating with cross-functional teams.

The role focuses on the research and development of optimization techniques for Machine Learning models specifically targeting GPUs and AI accelerators. This includes prototyping state-of-the-art algorithms, characterizing model performance, and integrating optimizations into the existing ML development lifecycle.

The engineer will collaborate with researchers to execute research operations using existing infrastructure, focusing on characterizing neural network quality, failure modes, and edge cases based on research data. Key tasks include managing concurrent experiments, reviewing results, suggesting improvements, and writing technical reports detailing qualitative and quantitative outcomes.

The role involves working collaboratively to improve models and iterate on novel research directions quickly, applying and extending proprietary algorithmic toolkits for unsupervised learning and perception problems at scale. Responsibilities also include executing development and maintenance of tools for deep learning experiments and deploying algorithms on internal and customer vehicle platforms.