Sr. AI Systems Engineer

As a Senior AI Systems Engineer, you'll be responsible for leading the development of innovative scene understanding systems. This includes calibrating extensive camera networks, translating the latest research into effective engineering solutions, and overseeing the deployment of technology in retail environments. Your role also involves mentoring team members and ensuring best practices in software development.

To apply for the Senior AI Systems Engineer position, a Bachelor’s or Master’s degree in computer science or a related field is required, along with six or more years of experience as a software engineer. Extensive knowledge of Python and experience with RESTful APIs, OpenAPI, gRPC, and Protobuf is essential. Familiarity with AI projects, specifically in computer vision, is also critical.

In addition to the required qualifications, beneficial skills for a Senior AI Systems Engineer include experience with asynchronous processes, containerized application management using Docker and Kubernetes, and knowledge of Azure services. Familiarity with multi-view stereo technologies and SLAM can also be very advantageous.

In this role, you will collaborate closely with various departments to ensure the smooth deployment, monitoring, and support of Scene Understanding Services across stores. Effective communication and clear organization will help promote interdisciplinary cooperation and project success.

The company culture for a Senior AI Systems Engineer is centered around collaboration, innovation, and continuous learning. You'll have the opportunity to work in a remote environment, where mentorship and knowledge sharing are strongly encouraged to foster professional growth and a cohesive team atmosphere.

When answering this question, outline a specific project, detailing the challenges faced, your strategic approach, and the successful outcomes. Highlight how your leadership and technical skills contributed to overcoming those challenges.

Demonstrate your commitment to continuous learning by discussing the resources you regularly consult, such as journals, online courses, or professional networks. Mention any conferences or industry events you attend to keep abreast of new trends.

Speak to your experience with Agile methodologies or other frameworks you find effective. Emphasize your ability to adapt these methodologies to fit the team’s needs and the importance of flexibility in today’s tech landscape.

Discuss a project where you strategically implemented machine learning techniques, focusing on how you integrated these into your systems. Speak to the methodologies used and the impact on the overall project goals.

Illustrate your leadership style by sharing a specific example where you successfully guided a team. Highlight how you balanced mentoring team members while driving project objectives and meeting deadlines.

Provide insights into specific projects that utilized 3D reconstruction or computer vision, detailing the techniques used and the results achieved. This will showcase your technical proficiency and relevance to the role.

Discuss the strategies you implement to maintain high standards of code quality, such as code reviews, pair programming, or continuous integration practices. Explain the importance of these practices in delivering reliable software.

Share your familiarity with specific debugging tools and monitoring solutions that you prefer, explaining how they contribute to maintaining system efficiency and reliability.

Explain how you have addressed scalability challenges in past roles, whether through design decisions or optimizing existing systems. Share metrics or experiences that illustrate the impact of those improvements.

Highlight your communication skills by explaining strategies for simplifying technical concepts for non-technical team members. Share past experiences where you successfully bridged the gap between technical and non-technical stakeholders.