Manager, Software Engineering

Overview

Lead the software infrastructure for distributed image computing clusters on the LS-SWIFT division, building and growing a team of software engineers and technical leaders.

What you'll do

• Define and drive the long-term vision and roadmap for distributed IMC software infrastructure supporting image computing clusters.
• Build, mentor, and grow a high-performing team of software engineers and technical leaders.
• Partner with product, hardware, and algorithm teams to align infrastructure capabilities with evolving image processing and AI requirements.
• Oversee the design and implementation of scalable, fault-tolerant distributed systems optimized for hybrid CPU/GPU workloads.
• Lead the end-to-end development of image computing platforms, from requirements gathering through deployment and maintenance, using best-in-class project management practices.
• Deliver robust software platforms and tools that empower engineers to develop, test, and deploy new image processing and deep learning algorithms efficiently.
• Spearhead the integration of traditional image processing and AI/DL techniques into a unified hybrid computing architecture, leveraging modern HPC technologies.

What you'll need

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related technical field.
• 10+ years of experience in software engineering.
• Proven track record in building and scaling distributed systems.
• Deep understanding of distributed computing frameworks and Linux systems programming.
• Proficiency in C++, Python, and/or other systems programming languages.
• Working knowledge of container technologies, orchestration frameworks (e.g., Kubernetes), and workflow automation.
• Strong grasp of software development best practices, CI/CD, and DevOps principles.
• Demonstrated ability to lead and drive functional teams.
• Excellent communication and stakeholder management skills.
• Passion for mentoring and developing engineering talent.

Nice to have

• Experience with image processing, computer vision, or AI/ML infrastructure.
• Preferably experience in HPC or cloud-native environments.
• Familiarity with GPU computing and hybrid CPU/GPU architectures.