The Backend department uses modern and innovative technologies to create our world-leading cybersecurity products, writing quality code. This role will identify necessary long-term technical improvements, clarify feature development plans, and oversee coding standards. They will mentor team members’ tech skills and establish a team's technical direction.

As a Staff SRE specializing in ML infrastructure, the primary responsibility is designing, developing, maintaining, and scaling the foundational infrastructure that enables Wikimedia's Machine Learning Engineers and Researchers to efficiently train, deploy, and monitor machine learning models in production.

Serve as the technical authority for Windows and Linux operating systems hosted on AWS virtual machines, with a focus on advanced cloud architecture, security, and automation. Architect scalable solutions, oversee OS patching and AWS strategies and resolve complex incidents. Drive performance optimization and implement cutting-edge security measures.

The Platform Engineer will join the Veeam Data Cloud team to provide a secure, reliable, and easy-to-use platform. The mission is to enable the teams to build, test, deploy, and monitor the VDC product by writing and maintaining code to automate the public cloud infrastructure, software delivery pipeline, and other enablement tools.

Manage the Tech Pod from a complete delivery perspective including working with your client to ensure project scope and alignment. Guide the Tech Pod both technically and career-wise, developing and using automation tools effectively to operate, manage, and scale production and development environments quickly. Participate in regular customer and internal EverOps scrums, monitoring environments while focusing on constant improvement.

Production Engineers keep Mysten's production infrastructure running smoothly even in the face of heavy traffic or attacks from adversaries, with expertise in release, deployment, logging, and monitoring processes. They have experience working with open-source frameworks and are obsessed with automation. They work closely with distributed systems experts.

Join EverOps as a Senior DevOps Engineer, managing customer's production cloud environments with a team of dynamic engineers. You'll use automation tools, participate in scrums, monitor environments, implement new technologies, and provide operational support, focusing on client satisfaction and high availability in a dynamic environment.

As a Data Scientist Specialist focused on MLOPs your mission will be to own the design and deployment of scalable systems to train, monitor, and expose machine learning models in production. You will own the design of system architecture to train, retrain, develop, monitor, and expose machine learning models; Design, develop, and maintain scalable infrastructure for ML pipelines and APIs.

We are looking for a Senior Cloud Engineer, ML Platform to lead the next phase of scaling our cloud-based machine learning (ML) and computer vision (CV) systems. This role is instrumental in moving beyond batch processing and toward real-time, high-throughput ML infrastructure supporting multi-model systems.

As our first Machine Learning Engineer focusing on MLOps, you will play a pivotal role in operationalizing our ML models, ensuring they are scalable, reliable, and easy to monitor. You’ll work closely with our data scientists, server engineers, and infrastructure teams to build feature stores for model development and enhance model deployment.