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Alluxio

Data management platform for AI workloads

Engineering Manager

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeFoster City, CA, USAIn-person

About the Job

Requirements

  • A bachelor's degree in Computer Science or a related technical field is required.
  • At least 7 years of experience as a strong hands-on engineer is required.
  • At least 3 years of experience leading or managing a team of engineers is required.
  • Experience working with at least two of the following is required: distributed and parallel systems, distributed storage systems, architecting large-scale software systems, or security software development.
  • Software development experience in one or more general-purpose programming languages, including Java, C/C++, or Go, is required.
  • Strong technical and conversational English language skills are required to work with global teams and customers.

Responsibilities

  • Oversee the delivery and support of engineering deliverables with high quality and on time, working closely with product management, customer success, field teams, and other key stakeholders.
  • Hire and support the career development of direct reports.
  • Work with the Engineering leadership team to define and improve processes, culture, and best practices to align with the company's growth.
  • Build scalable distributed systems to support the vision of Cloud Data Orchestration.
  • Spend at least 25% of the time on hands-on design and coding.
  • Lead, guide, and evaluate the work of Engineering direct reports.
  • Develop and grow talent through mentoring and technical assignments.
  • Develop and refine best practices and processes to guide teams, leading by example.

About the Company

Alluxio

Data management platform for AI workloads

Alluxio provides a data management platform for AI and ML workloads. It sits between compute and storage with two products, Alluxio Enterprise Data and Alluxio Enterprise AI, offering a unified interface to manage data across clouds, regions, and storage systems. This helps reduce data silos, minimize data movement, and improve data access latency, boosting GPU utilization and accelerating model training and serving. The goal is to simplify data engineering, cut infrastructure complexity and cost, and help enterprises accelerate their AI initiatives.

Company size
51-200
Company stage
Series C
Total funding
$81.5M
Headquarters
San Mateo, CA
Founded
2015

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