Software Engineer, Machine Learning Platform

About Zscaler

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.

Job Description

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

Responsibilities:

• 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.

• Very strong algorithm, data structure, computer science foundation
• Industry experiences in software engineering, building quality software by writing robust interfaces, considering design principles, and applying sound testing practices
• 2+ years of industry experiences in Python and SQL
• Industry experience using distributed data processing such as Spark, BigQuery, or Apache Beam
• Industry experience with various cloud services (such as AWS, Google, Azure) and ML automation platforms (such as Kubeflow).
• Industry experience with Docker, Kubernetes, and event messaging such as Kafka, RabbitMQ, etc
• Bachelor or Master's Degree in Computer Science required, data science concentration is a plus; PhD is preferred
• Excellent understanding of operating systems and distributed systems.
• Excellent interpersonal, technical, and communication skills
• Very good business sense.
• Ability to learn, evaluate, and adopt new technologies fast.

• Experience with Go, C++, or Javascript
• Experience with setting up SQL/NoSQL database such as Postgres, MongoDB, Redis, and table schema
• Experience with machine learning or deep learning related toolset/frameworks, such as Pandas, Numpy, Scikit-learn, TensorFlow, PyTorch, etc.
• Familiarity with networking and networking security.