Director, AI / Perception

The Director, AI / Perception at Pano AI is responsible for leading a dynamic team focused on enhancing computer vision-based fire detection systems. Key responsibilities include developing algorithms for fire detection and geolocation, establishing performance metrics, coordinating with product management, and ensuring robust performance in various conditions. This role is essential for driving research initiatives to improve detection speed and accuracy while staying updated on the latest advancements in computer vision.

Candidates for the Director, AI / Perception role at Pano AI should ideally possess a PhD in computer vision or machine learning, with a Master's degree combined with significant applied research experience being acceptable. Additionally, the role requires at least 7 years of industry experience in AI research and development, specifically 3 years concentrated on computer vision. Leadership experience of 5 years is also essential, along with familiarity with deep learning frameworks such as TensorFlow and PyTorch.

The AI / Perception team at Pano AI plays a crucial role in enhancing fire detection capabilities through the development of advanced algorithms that process real-time visual data. This team focuses on creating precise geolocation methods and improving overall system accuracy, impacting how quickly and effectively fire authorities respond to incidents. By leveraging AI and machine learning, they ensure that vital data is available to firefighting teams, aiding in decision-making and response efficiency.

The systems developed by the Director, AI / Perception at Pano AI must perform reliably under various challenging conditions. This includes nighttime settings, partial visibility scenarios, and fluctuating weather conditions. A focus on robust algorithm performance ensures that fire identification and geolocation are accurate regardless of environmental challenges, which is crucial for timely and effective fire response.

The compensation range for the Director, AI / Perception position at Pano AI is between $214,000 and $285,000 annually. This range is influenced by various factors, including the individual's qualifications, experience, and geographic location. Additionally, this position also offers stock options, comprehensive medical benefits, unlimited paid time off, and a matching 401k, ensuring a well-rounded compensation package.

To effectively answer this question, highlight specific projects where you've applied computer vision techniques, particularly those related to object detection or real-time video processing. Discuss the technologies you used, such as TensorFlow or PyTorch, and demonstrate how your contributions led to measurable improvements in project outcomes.

When answering this question, focus on a specific challenge that illustrates your leadership skills and problem-solving abilities. Discuss the steps you took to address the issues, how you collaborated with team members, and the ultimate results. Use this example to showcase your ability to navigate technical challenges while leading effectively.

Your response should include details about your approach to establishing performance metrics and testing protocols. Discuss methodologies like cross-validation, model tuning, and the use of diverse datasets for training. Emphasize the importance of continuous learning and adapting models based on real-world feedback.

Explain your strategies for staying current, which could include following key journals, attending conferences, or participating in online courses related to AI and computer vision. Mention any specific networks or communities you engage with to share insights and best practices, stressing your commitment to continuous learning in a fast-paced field.

Collaborating effectively with product management involves understanding their goals and aligning AI initiatives with those objectives. Discuss how you communicate technical constraints and opportunities clearly while integrating feedback from product managers. Provide examples of previous collaborations that resulted in successful product development or improved processes.

Provide details about the frameworks and libraries you’ve worked with, such as TensorFlow, Keras, or OpenCV. Discuss projects where you utilized these tools, including your role in designing models and implementing solutions. Highlight any particularly innovative applications or optimizations you've achieved.

Your response should demonstrate a thoughtful approach that combines empathy with accountability. Discuss strategies like setting clear expectations, providing constructive feedback, and supporting skill development. Share a specific incident where your approach helped a team member improve their performance.

Outline a step-by-step strategy, starting with understanding the problem domain and defining key performance metrics. Discuss data collection (e.g., diverse imagery datasets), model selection, training procedures, and iterative testing. Emphasize the importance of stakeholder collaboration and real-world testing to validate the algorithm.

Highlight specific metrics or outcomes from your past roles, such as increased accuracy in detection rates or reduced latency in response times. Use quantifiable data when possible, and link your past successes to how they will benefit Pano AI’s mission and objectives in wildfire detection.

Describe your prioritization strategies, emphasizing frameworks like RACI (Responsible, Accountable, Consulted, Informed) to clarify roles. Discuss how you assess project impact, resource availability, and timelines to ensure that critical tasks receive appropriate attention while maintaining team alignment on goals.