Principal ML Infrastructure Engineer

Position Overview

• Location Type: Remote
• Employment Type: Full-time
• Salary: Not specified

Upwork is the world’s largest work marketplace, connecting businesses with highly skilled professionals worldwide. From entrepreneurs to Fortune 100 enterprises, companies trust Upwork’s platform to access expert talent, leverage AI-powered work solutions, and drive meaningful business outcomes. Upwork’s AI-powered platform has facilitated over $20 billion in economic opportunity for professionals worldwide. With professionals spanning 10,000+ skills, including AI and machine learning, software development, sales and marketing, customer support, finance and accounting, and more, Upwork empowers businesses of all sizes to scale, innovate, and build agile teams.

The Machine Learning Infrastructure & Data team is responsible for architecting and building the foundational ML systems and tools that enable efficient development, deployment, and management of machine learning models at scale.

As a Principal ML Infrastructure Engineer in the Machine Learning Infrastructure & Data team, you will play a pivotal role in designing, developing, and maintaining robust and scalable ML infrastructure components to support the company's machine learning initiatives. You will collaborate closely with cross-functional teams including machine learning researchers, data scientists, and software engineers to build state-of-the-art platforms and tools that accelerate the development and deployment of machine learning models.

Requirements

• Not specified

Responsibilities

• Own technical workstreams from start to finish, contribute to the team’s product roadmap, and be responsible for major technical decisions and tradeoffs.
• Effectively participate in team’s planning, code reviews, and design discussions.
• Consider the effects of projects across multiple teams and proactively manage conflicts.
• Work together with partner teams to achieve cross-departmental goals and satisfy broad requirements.
• Design, implement, and optimize distributed systems and infrastructure components to support large-scale machine learning workflows, including data ingestion, feature engineering, model training, and serving.
• Develop and maintain frameworks, libraries, and tools to streamline the end-to-end machine learning lifecycle, from data preparation, model training, evaluation, deployment, and monitoring.
• Architect and implement highly available, fault-tolerant, and secure systems that meet the performance and scalability requirements of production machine learning workloads.
• Collaborate and publish with machine learning researchers and data scientists on novel research and translate research into scalable and efficient software solutions.
• Stay current with the latest advancements in machine learning infrastructure, distributed computing, and cloud technologies, and integrate them into our platform to drive innovation.
• Mentor teammates, conduct code reviews, and uphold engineering best practices to ensure the delivery of high-quality software solutions.

Application Instructions

• Not specified