Senior AI Perception Engineer

At CompScience, the Senior AI Perception Engineer plays a pivotal role in developing and deploying advanced AI systems for video understanding. Key responsibilities include designing video-centric AI models for object detection, activity recognition, and scene understanding, as well as integrating and fine-tuning large language models for enhanced video analysis. You'll also need to optimize models for real-time performance and maintain the full AI pipeline, from data ingestion to model monitoring.

To be considered for the Senior AI Perception Engineer position at CompScience, candidates need a Master's or PhD in Computer Science or a related field, as well as 6+ years of hands-on experience developing end-to-end computer vision models. Proficiency in programming with Python and C++, familiarity with PyTorch and a solid grasp of machine learning principles are crucial. Strong problem-solving abilities and effective communication skills are also vital for success in this collaborative role.

The Senior AI Perception Engineer at CompScience significantly contributes to workplace safety by utilizing proprietary computer vision technologies to analyze risks in various environments. By designing and deploying AI models that assess video streams and detect potential hazards, the engineer ensures that actionable insights are provided to reduce accidents and promote a safer work environment—ultimately aligning with CompScience's mission to save lives.

As a Senior AI Perception Engineer at CompScience, you'll work primarily with cutting-edge technologies in computer vision and AI. You’ll utilize frameworks like PyTorch for model development, apply large language models for enhanced video understanding, and leverage deep learning techniques to tackle complex video analysis tasks. Understanding the Linux environment for optimal hardware setup will also be beneficial in this role.

At CompScience, a Senior AI Perception Engineer can expect a fast-paced, remote-first work environment that encourages creativity and innovation. You'll be part of a supportive team that values diverse perspectives and collaborative problem-solving. With flexible work arrangements, including remote work options, CompScience nurtures a culture where your ideas can quickly turn into impactful solutions for workplace safety.

Certainly! When answering this question, focus on specific projects where you utilized computer vision technologies. Discuss the models you designed, the challenges faced during deployment, and how you ensured their performance in production environments. Highlight any metrics or results that demonstrate the impact of your work.

In your response, outline the key methodologies you employ, such as pruning, quantization, and efficient architecture design. Explain how you assess real-time performance via benchmarking and optimization techniques, and provide examples of how you've successfully implemented these strategies in past projects.

Discuss your experience with LLMs, emphasizing how you fine-tune them for specific tasks in video analysis. Explain your approach to data alignment, ensuring appropriate context for video events, and how you've successfully enhanced video classification or descriptive outputs using LLMs in past projects.

Share your preferred tools and libraries, such as OpenCV, PyTorch, or TensorFlow, explaining the reasoning behind your choices. Highlight the specific functionalities or features of these tools that have helped you achieve successful outcomes in your AI projects.

Use the STAR method to structure your response, detailing the situation, the task at hand, your actions, and the results. Focus on a challenging technical issue related to model performance or data integration, and discuss the problem-solving strategies you employed, including collaboration with team members or research on best practices.

Explain your approach to collaboration, emphasizing clear communication, setting expectations, and regular progress updates. Provide examples of successful teamwork experiences where you translated technical requirements into actionable deliverables that met product goals.

Discuss emerging technologies such as increased real-time video analytics, AI-embedded smart devices, and their potential impact on workplace safety. Reference how these advancements can improve risk detection and decision-making processes, aligning with your role in advancing CompScience's mission.

Mention specific journals, conferences, or online communities you follow to stay informed. Highlight any continuous learning practices, such as online courses or collaborative studies with peers, that you engage in to maintain a competitive edge in the fast-evolving field of AI.

Detail a scenario where you guided junior engineers or data scientists through a project, addressing their challenges and sharing your knowledge. Highlight the tools or resources you provided and how this mentorship contributed positively to their professional growth and the project's success.

Discuss your perspective on responsible AI development, emphasizing the importance of fairness, transparency, and accountability. Talk about approaches you use to ensure ethical practices in your work, such as bias mitigation and user-centric design considerations.