Machine Learning Infrastructure Engineer
October 17
Description
• Remote position (Only candidates located in Argentina or Uruguay will be considered) • At Ryz Labs, we are looking for an experienced Machine Learning Infrastructure Engineer to join one of our client's teams. • Their ML platform is a critical part of their operations, enabling them to train and test a wide range of ML models for various real-world tasks. • It also allows them to analyze vast amounts of data from terabytes of sensor recordings we capture daily. • As part of the ML Infra team, you'll help them build and enhance their platform. • They work with technologies such as Apache Beam (Dataflow) for pipelines, Bazel for builds, BigQuery via dbt, MongoDB and GCS for storage, Kubernetes for service deployment, and Airflow for workflow management. • Develop and maintain ML infrastructure, such as sensor data ETL pipelines, hard example data mining, continuous training pipelines, annotation platform, etc. • Develop MLOps system for managing lifecycle of ML cloud training and inference as a service pipelines. • Continuously improve ML model development, management and deployment processes. • Work together with ML engineers, design metrics for ML tasks to mine sensor data of interest. • Design and implement algorithms, such as collaborative filtering, active learning, etc., to rank/score annotation candidates. • Work with annotation provider on setting up the annotation process, quality control and feedback loops. • Make sensor data and its derivatives widely discoverable and accessible for Robotics Engineers across the entire company.
Requirements
• BS or MS in computer science with focus in data engineering and large scale ML systems • 3+ years of industry experience building, running and improving large-volume ML training and validation pipelines. • Experience with building native cloud applications • Experience with large scale data processing pipelines in production. • Proficient in at least one of the following languages: C++, Python, or Go. • Hands-on experience and good knowledge of Computer Vision and Deep Learning. • Strong tendency to automate own and others’ workflows. • Experience with data discovery and visualization tools like Voxel51, Facets • Experience with database systems like BigQuery, MongoDB • Experience with Nvidia Jetson platform, e.g. CUDA, TensorRT, etc. • Experience with Big Data products such as Apache Beam/Spark/Hadoop, GCP BigQuery, AWS Redshift.
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