ML Engineer - Intern

Company Description

Zscaler (NASDAQ: ZS) accelerates digital transformation so that customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange is the company’s cloud-native platform that protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. 

With more than 10 years of experience developing, operating, and scaling the cloud, Zscaler serves thousands of enterprise customers around the world, including 450 of the Forbes Global 2000 organizations. In addition to protecting customers from damaging threats, such as ransomware and data exfiltration, it helps them slash costs, reduce complexity, and improve the user experience by eliminating stacks of latency-creating gateway appliances. 

Zscaler was founded in 2007 with a mission to make the cloud a safe place to do business and a more enjoyable experience for enterprise users. Zscaler’s purpose-built security platform puts a company’s defenses and controls where the connections occur—the internet—so that every connection is fast and secure, no matter how or where users connect or where their applications and workloads reside.

Zscaler enables the world’s leading organizations to securely transform their networks and applications for a mobile and cloud-first world. Applications have moved from the data center to the cloud and users are connecting to their workloads from everywhere, but security has remained anchored to the data center. Zscaler is redefining security by moving it out of the data center and into the cloud.

The Zscaler Zero Trust Exchange uses software-defined business policies, not appliances, to securely connect the right user to the right application, regardless of device, location, or network. Zscaler operates 4 pillars of Trust Exchange. Zscaler Internet Access™ which scans every byte of traffic to ensure that nothing bad comes in and nothing good leaks out. Zscaler Private Access™ offers authorized users secure and fast access to internal applications hosted in the data center or public clouds—without a VPN. Zscaler Cloud Workload Protection, to identify and remediate risks associated with customer’s cloud infrastructure. Zscaler Workload Segmentation provides micro-segmentation of processes across multiple systems with Machine Learning based policy.

Zscaler services are 100% cloud delivered and offer the simplicity, enhanced security, and improved user experience that traditional appliances or hybrid solutions are unable to match. Used in more than 185 countries, the Zscaler multi-tenant, distributed security cloud protects thousands of customers from cyberattacks and data loss, enabling customers to embrace the agility, speed, and cost containment of the cloud—securely.

 

Job Description

As a Junior Software Engineer, Machine Learning you will work in an award-winning team that does full-lifecycle full-stack Machine Learning platform development.   

• You will be working with the massive scale of network data, security data, and enterprise data every day. 

• You need to have a passion for building out tools and platforms, processing and analyzing data at scale, and solving real-world business problems.

• You may not have prior data science and ML background but need to build up knowledge in this area and tremendous curiosity in how the data can and will be utilized by the data scientist. 

• As a backend software engineer within our Machine Learning platform, your primary responsibilities include the following:
  • You will help build large-scale distributed systems to support the Machine Learning pipeline, including data collection, feature engineering, model training, model evaluation, model deployment, and real-time service. 
  • You will apply analytical and math/statistics skills to stay on top of data and to ensure results are coherent and reliable.
  • You will solve complex real-world business problems (e.g., threat detection, automation, and business intelligence) by working closely with various stakeholders including data scientists, product management, and product engineering teams.

Qualifications

• Solid understanding of algorithms, data structures, computer science foundation 
• Experience in software engineering, building quality software by writing robust interfaces, considering design principles, and applying sound testing practices
• Experience with Python and SQL
• Experience using distributed data processing such as Spark, BigQuery, or Apache Beam
• Experience with various cloud services (such as AWS, Google, Azure) and ML automation platforms (such as Kubeflow). 
• Industry experience with Docker, Kubernetes, and event m