As an AI Software Team Lead (Hands-On), you will lead a team of software engineers responsible for Axon’s production AI infrastructure and real-time computer vision pipelines. This is a hands-on leadership role, combining technical ownership, architectural decision-making, and active development. You will work closely with research, hardware, and product teams to transform cutting-edge AI algorithms into robust, high-performance production systems deployed on edge environments.

What You'll Do:

Technical Leadership

• Lead and mentor a team of AI software engineers (2–6 engineers).
• Own the team’s technical roadmap, architecture, and engineering standards.
• Drive best practices in system design, testing, performance optimization, and production reliability.
• Conduct code reviews and actively contribute to the codebase (approximately 30–50% hands-on development).

Delivery & Execution

• Own end-to-end delivery of production AI and real-time video processing pipelines.
• Define priorities, break down initiatives into actionable tasks, and ensure execution.
• Collaborate closely with research teams to productionize state-of-the-art AI models.
• Oversee deployment across a diverse range of edge hardware platforms.

System & Performance Ownership

• Optimize runtime efficiency of AI models and classical computer vision algorithms.
• Ensure scalability, low latency, and high reliability of mission-critical systems.
• Maintain deep familiarity with the production code and system architecture.

Qualifications (Must)

• BS/MS in Computer Science or related field.
• 7+ years of software engineering experience in industrial or academic settings.
• 2+ years of experience leading or mentoring engineers.
• Strong proficiency in Python.
• Strong experience developing in Linux environments.
• Proven experience designing and delivering production systems.
• Strong system design and architectural skills.
• Ability to thrive in a dynamic early-stage environment.

Qualifications (Advantages)

• Deep understanding of state-of-the-art computer vision and deep learning concepts.
• Experience with leading AI systems into production.
• Experience with video protocols and streaming tools (GStreamer, FFmpeg).
• Experience with hardware integration tools (ROS, serial communication, camera calibration tools).