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We are seeking an experienced Data Engineer to join our Data Platform Engineering group at our company. This group is responsible for providing the technological solutions that best fit the business needs of various stakeholders, including product teams, BI analysts, marketing analysts, finance, data scientists, backend teams, and more.
What you will be doing
Build and own the infrastructure and services required for processing and storing data from a wide variety of sources, including mobile apps and internal/external data producers.
Design and build processes to integrate a wide range of edge systems across multiple cloud providers and handheld platforms reliably.
Assist data consumers across our company in defining requirements and refining existing processes using a wide variety of tools.
Define and own the systems and processes that deliver data to the data warehouse, while maintaining these systems and the warehouse itself.
Identify bottlenecks in internal data processes and resolve them by automating workflows, optimizing data delivery and storage, and redesigning infrastructure for greater scalability.
Integrate new tools and solutions to empower the companys data consumers and help assimilate them effectively.

As an ML Engineer, you will play a crucial role in shaping the system design, backend architecture, and overall performance of our companys ML inference systems. We collaborate with cross-functional teams, including designers, developers, and researchers. The role offers the chance to significantly impact our companys core production systems. Youll have the opportunity to develop and optimize critical backend features and work across diverse technical areas, contributing directly to the company's success. This hands-on role involves working extensively with Python, ComfyUI, system optimizations, and cloud infrastructure.
This position is perfect for a skilled backend engineer passionate about driving efficiency and innovation in machine learning.
Responsibilities
Design and optimize backend systems to support efficient ML model inference
Work with cloud infrastructure, networking, and both CPU and GPU architecture
Enhance the efficiency and observability of our inference engine, supporting various high-demand applications
Collaborate closely with engine users, designers, developers, and researchers to refine and improve system capabilities
Contribute to the ComfyUI codebase, ensuring a scalable, user-friendly interface for machine learning operations
Navigating and contributing to a large, complex existing codebase
Addressing complex issues with creativity and efficiency.