The Security Analytics Engineering group is looking to hire a Sr. Machine Learning to help lead the development of cutting-edge machine learning models to better protect our users from malicious actors and improve our customer product experience. We are looking for a hands-on engineering leader with deep data science and software engineering expertise who can help architect and own the platform for deploying and tuning the machine learning models used to protect user authentication and security.
The teams focuses on using analytics, machine learning, and threat research on petabyte-scale data to deliver security value to millions of business users across thousands of businesses. The product portfolio is a part of the rapidly growing Carbon Black Cloud platform that delivers next-generation endpoint protection capabilities from the cloud. Now with the full resources of VMware, you have the opportunity to make an impact and build upon Carbon Black’s success.
Design, build, and develop state of art machine Learning system infrastructure core components and architecture of a machine learning platform for VMware CB SBU to create, train and deploy ML models
Automate the day to day operational support for model training and model serving pipelinesCreate monitoring solutions that allow effective system accuracy, performance and enable troubleshooting of production ML models.
Identify gaps and evaluate relevant tools and technologies as needed to improve processes and systems, leveraging open-source and cloud computing technologies to build effective solutions.
Collaborate with data scientists, data engineers, product teams, and other key stakeholders and drive ML platform projects from conception to completion and production monitoring.
6+ years strong experience in large scale distributed systems, Data Engineering, MLOps, Machine Learning and Data Science areas
Experience developing data pipelines and orchestrating the deployment of ML models for production ready systems .
Must have working experience to MLOps tools such as KubeFlow, MLFlow, Metaflow, or Sagemaker
Infrastructure as code - Terraform experience
You can use AWS service and have a solid understanding of VPC, ALB/ELB, EC2, Route53, Kinesis, IAM, and other AWS concepts.
Strong understanding of containerization (Docker) and container-orchestration systems like Kubernetes; experience with data workflow managers such as Airflow is a plus
Experience with stream processing technology Kafka, Spark, Samza, Flink, etc.
Working proficiency in SQL. Experience with big-data processing engines (Hive, Tez, Spark, Presto, Athena) is a plus.
Familiarity with machine learning frameworks (like Keras or Tensor flow)
Category : Engineering and Technology
Subcategory: Software Engineering
Experience: Manager and Professional
Full Time/ Part Time: Full Time
Posted Date: 2021-05-25