Lead Machine Learning Infrastructure Engineer - Infrastructure & Data

Position Overview

• Location Type: Remote
• Employment Type: Contract
• Salary: Not specified

Upwork is the world’s work marketplace. We serve everyone from one-person startups to large, Fortune 100 enterprises with a powerful, trust-driven platform that enables companies and talent to work together in new ways that unlock their potential. Last year, more than $3.8 billion of work was done through Upwork by skilled professionals who are gaining more control by finding work they are passionate about and innovating their careers.

The Machine Learning Infrastructure & Data team is responsible for architecting and building the foundational systems and tools that enable efficient development, deployment, and management of machine learning models at scale. As a Lead Machine Learning Infrastructure Engineer, you will be pivotal in designing, developing, and maintaining robust and scalable infrastructure components to support Upwork’s machine learning initiatives. You will work 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

• Strong technical expertise in designing and building scalable ML infrastructure.
• Experience with distributed systems and cloud-based ML platforms.
• Proficiency in programming languages such as Python, Java, or Scala.
• Deep understanding of ML workflows, including data pipelines, model training, and deployment.
• Passion for innovation and eagerness to implement the latest advancements in ML infrastructure.
• Strong problem-solving skills and ability to optimize complex systems for performance and reliability.
• Collaborative mindset with excellent communication skills to work across teams.
• Ability to thrive in a fast-paced, dynamic environment with evolving technical challenges.

Responsibilities

• 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 that streamline the end-to-end machine learning lifecycle, from data preparation and experimentation to model 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 with machine learning researchers and data scientists to understand their requirements and translate them into scalable and efficient software solutions.
• Stay current with advancements in machine learning infrastructure, distributed computing, and cloud technologies, integrating them into our platform to drive innovation.
• Mentor junior engineers, conduct code reviews, and uphold engineering best practices to ensure the delivery of high-quality software solutions.

Application Instructions

• Not specified.