Senior AI/ML Computer Vision Engineer - job 2 of 2

As a Senior AI/ML Computer Vision Engineer at TP-Link Systems Inc., you will lead the development of machine learning-based computer vision pipelines for real-time object detection, tracking, and classification using various sensors like cameras, radar, and LiDAR. You will also optimize models for embedded hardware, implement techniques like quantization and pruning, and collaborate with the cloud team on edge-to-cloud data pipelines, ensuring our smart devices are intelligent and efficient.

Candidates for the Senior AI/ML Computer Vision Engineer role at TP-Link Systems Inc. should have a Master's degree in computer science or a related field, along with over 7 years of experience in embedded AI and computer vision. Proficiency in programming languages like C, C++, and Python, paired with expertise in deep learning frameworks such as TensorFlow and PyTorch, is essential for optimizing machine learning models for deployment on resource-constrained edge devices.

TP-Link Systems Inc. offers a competitive salary range of $130,000 to $180,000 for the Senior AI/ML Computer Vision Engineer role. This reflects our commitment to attracting top talent who will contribute to our mission of enhancing connectivity through innovative technology.

As a Senior AI/ML Computer Vision Engineer at TP-Link Systems Inc., you'll enjoy a robust benefits package that includes fully paid medical, dental, and vision insurance, contributions to 401k funds, a free gym membership, quarterly team-building events, and bi-annual reviews with potential pay increases. We also provide free snacks, drinks, and lunch on Fridays to foster a positive work environment.

At TP-Link Systems Inc., we believe that a vibrant and inclusive company culture fuels innovation and collaboration. As a Senior AI/ML Computer Vision Engineer, you'll be part of a team that values diverse perspectives, ensuring all voices are heard and respected. We strive to create a supportive and growth-oriented workplace, committed to equal employment opportunities and fostering a spirit of entrepreneurial innovation.

When addressing your experience with machine learning model deployment on edge devices during the interview, focus on specific projects where you've successfully optimized models for low-latency inference. Discuss the frameworks you've used, such as TensorFlow Lite or OpenVINO, and any challenges you faced in limited hardware environments, highlighting how you overcame them.

In response to questions about optimizing deep learning models, discuss techniques like quantization, pruning, and model compression. Provide concrete examples from your previous work where you applied these techniques to reduce model size and improve inference speed, ensuring your answer reflects a strong understanding of real-time performance requirements.

When discussing multi-sensor fusion, explain how you integrate data from various sources such as cameras, radar, and LiDAR to enhance device intelligence. You could highlight specific algorithms or frameworks used in your past projects that demonstrate your ability to create robust and accurate computer vision systems.

Detail your proficiency in programming languages like C, C++, and Python, emphasizing specific projects where you've used these languages. Describe how you've applied them to develop machine learning models and manage embedded AI systems, making sure to illustrate your programming skills with practical examples.

When asked about managing multiple projects, outline your organizational strategies, such as prioritizing tasks based on deadlines and impact. Share examples that illustrate your effectiveness in juggling various responsibilities and how you maintain communication with team members and stakeholders to ensure project success.

Share a specific project where you led the development of a computer vision application, detailing your role and the strategies employed. Discuss the challenges faced, solutions implemented, and the measurable outcomes, showcasing your leadership and technical skills in the field of AI/ML.

Explain your approach to ensuring efficiency in models by discussing techniques you employ for quantization, pruning, and selecting appropriate architectures for resource-constrained environments. Highlight your ability to test and validate these models under real-world conditions to ensure they meet performance expectations.

Share your insights on the potential advancements in AI-powered smart home technology. Discuss trends you foresee, like improved user interaction with intelligent devices or enhanced security features, and align your vision with TP-Link's commitment to innovation and excellence in connectivity.

Talk about your commitment to continuous learning through attending conferences, participating in relevant online courses, or engaging with communities in AI and computer vision. Show that you actively seek out new knowledge to apply innovative solutions in your work.

Discuss potential challenges such as limited processing power, memory, and energy constraints in edge devices. Share your strategies for overcoming these challenges, including optimization techniques and innovative engineering approaches you've utilized in past projects.