Fully Remote Sr. or Lead Machine Learning Engineer

Position: Sr. or Team Lead Machine Learning Engineer, Full Time

Location: Fully Remote anywhere in the USA or optional office location in SF

At Cisco Meraki, we know that technology can connect, empower, and drive us. Our mission is to simplify technology so our customers can focus on what's most meaningful to them: their students, patients, customers, and businesses. We’re making networking easier, faster, and smarter with technology that simply works.

As a Machine Learning engineer on the Insight team, you will collaborate with firmware and full stack engineers to design, plan, and build customer-facing analytics tools. Meraki's cloud-managed model offers a unique opportunity to draw upon data from millions of networks across our wide ranging customer base. The goal is to use the rich telemetry data available from these networks and combine it with the power of machine learning and the cloud to build an analytics engine that can provide intuitive, yet detailed insights into the performance of customer networks.

What you can expect:

• Build a system that ingests real-time streams of network performance data and identifies network performance degradation, optimizing for both low latency and few false positives
• Design models that predict network performance for customers to help them understand their network performance issues
• Work with firmware and backend engineers to design uplink selection algorithm for SD-WAN
• Collaborate with full stack engineers to make intuitive data visualizations and integrate predictions seamlessly and powerfully into the user experience
• Build, maintain, and monitor data pipelines and infrastructure for training and deploying models

What we're ideally looking for:

• 5+ years of relevant industry experience
• Advanced training in mathematics, statistics, and modeling
• Experience programming in Python AND some other programming language like scala, golang, ruby, etc.
• Experience working with algorithms and building models for supervised and unsupervised learning.
• Experience using data processing and ML libraries such as Pandas, Scikit-Learn, Tensorflow, Keras, etc.
• Experience working with distributed computing engines like Apache Spark, etc. and real time data streaming services like Amazon Kinesis.
• Experience implementing and monitoring data pipelines.