Lead Machine Learning Infrastructure Engineer

2 days ago

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Cobalt AI

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Description

• Design and implement robust machine learning pipelines to crunch on a large number of simultaneous video streams. • Improve the performance and reliability of our GPU workloads, on the edge and in the cloud. • Design and implement scalable infrastructure for deploying CUDA-enabled ML models to our expanding fleet of edge devices. • Discover, evaluate, and integrate cutting-edge computer vision models/algorithms to enable better decision making in time-sensitive contexts. • Critically assess and address networking and data storage security risks for the edge processors and their integration with our backend. • Lead a mix of senior and junior engineers by example, establishing and maintaining best practices in ML infrastructure development. • Lead design reviews and gather input from multiple teams to ensure high-quality outcomes. • Tackle challenging interdisciplinary problems within our deep tech stack. • Deploy changes with immediate impacts on our product, worldwide.

Requirements

• Minimum 5+ years of experience in software engineering, with a focus on ML infrastructure design. • Experience leading teams or managing projects. • Demonstrated expertise in ML infrastructure development and best coding practices for Python, C++, Rust or equivalent. • Experience with pipeline stability and resilience using tools, such as Datadog or equivalent. • Passionate about delivering high-quality products that exceed user expectations. • Eager to work with, mentor, and learn from peers through code reviews, design documents, and pair programming. • Enthusiastic about diving into different areas of technology and problem-solving. • Must be authorized to work in the United States. • Bonus Skills: • Experience with generative AI technologies. • Experience with event streams, like kinesis or equivalent. • Familiarity with the Nix package manager and its ecosystem. • Experience working in a fast-paced startup environment. • Hands-on experience with AWS and DevOps practices. • Expertise in networking and security protocols. • Experience building and scaling high availability distributed systems. • Background as a full-stack engineer.