Software Engineer - Machine Learning

Headquarters: Palo Alto, Ca URL:

ABOUT THE JOB

As a Software Engineer on Polaris Radar, you will be working on Rubrik's cloud-based anomaly and ransomware detection SaaS product, which leverages machine learning to power its core detection algorithms ().

Responsibilities:

• Analyze experimental data, communicate analysis results, and develop product improvements
• Design, code, deploy, and support scalable machine learning pipelines within a business context
• Work closely with the team to scale and improve the performance of core machine learning systems and increase the team’s machine learning productivity
• Full stack ownership of projects from design to implementation and deployment

ABOUT YOU

• Able to identify scalable & elegant solutions to complex problems that work at scale
• Interest in working on machine learning systems; prior work or educational experience with machine learning is preferred
• Experience with common machine learning techniques such as data pre-processing, feature engineering, and training/evaluation of classification and regression models
• Experience with machine learning software packages (e.g. numpy/pandas, scikit-learn, TensorFlow)
• Knowledge of OS internals, databases would be useful
• Large distributed systems design and development experience is a bonus

ABOUT THE TEAM

Our team builds products to address Rubrik's holistic ransomware response strategy. We make it easier and faster to recover from security attacks while providing greater intelligence on how an incident impacted enterprises' global applications and data.

Our engineers are self-starters, driven and can manage themselves. We believe in giving engineers responsibility, not tasks. Our goal is to motivate and challenge you to do your best work. We encourage each engineer to try out new ideas. A lot of such ideas have become our products. All ideas, whether they are coming from newly graduated students or seasoned engineers, are all respected and valued the same way.

To apply: