
Machine Learning Infrastructure, Software Engineer
Machine learning infrastructure team’s job is to provide an easy to use, flexible, elastic, and highly available platform to our machine learning engineers and enable them to train, debug, deploy, and monitor their models. You will design solutions for machine learning infrastructure that will support the current and future needs of our business. Many interesting and unique use cases that require innovation both in the application and infrastructure level, makes this a challenging and dynamic job. You will apply your system software and machine learning knowledge to build scalable, reliable, and easy-to-use machine learning workflows. You will enable machine learning teams to perform scalable training, evaluation, inference, debugging, monitoring in the cloud and on prem. You will enable software engineers across the company to use machine learning solutions.
5+ years of industry experience building ML infrastructure at scale (3+ years for junior level candidates)
Experience writing and maintaining high-quality production code
Deep knowledge of Linux operating system and proficient in Bash
Experience using AWS and/or other major cloud platforms to build data-intensive infrastructure
Working knowledge of publicly available ML platforms such as Sagemaker, Kubeflow, and MLflow (Preferred)
Python
C++
Redis
Bash
Jenkins
Kafka
Tensorflow
Ansible
Keras
Terraform
Sci-kit Learn
Effective Time Management
RocksDB
Verbal communication
written communication
Adaptability
Problem-Solving
Detail Oriented and Organized
Multi tasker
Driven for excellence
According to JobzMall, the average salary range for a Machine Learning Infrastructure, Software Engineer in 50 Beale St, San Francisco, CA 94105, USA is $153,000 - $211,000.
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