Lead Computer Vision Engineer

As a Lead Computer Vision Engineer at CompScience, your primary responsibilities include designing and developing advanced machine learning and deep learning systems specifically aimed at video data analytics. You will lead the way in developing and deploying computer vision algorithms for essential object and activity detection, and oversee testing and validation processes. Additionally, you'll manage the project roadmap and mentor junior engineers to foster their professional growth, all while ensuring that your projects align with our mission of preventing workplace injuries.

To qualify for the Lead Computer Vision Engineer position at CompScience, candidates should possess a Master's or PhD in Computer Science, Computer Engineering, or a related field. Also, having over 12 years of experience in computer vision tasks such as pose detection, object detection, and tracking is essential. Strong programming skills in C++ and Python, along with familiarity with libraries such as PyTorch, YOLO, and OpenCV, are also required for success in this role.

At CompScience, you can expect a vibrant and fast-paced startup environment. We embrace a remote-first culture, where teamwork thrives and diverse ideas are celebrated. You will have the opportunity to wear multiple hats, contribute to impactful projects, and see your ideas realized in real-time. Given our commitment to innovation, you’ll engage with a team passionate about leveraging technology to save lives and enhance workplace safety.

The Lead Computer Vision Engineer plays a critical role in enhancing workplace safety at CompScience through the development of cutting-edge analytics solutions. By designing ML and deep learning systems that analyze video data, you will enable the detection of risky behaviors and activities in real-time. This capability not only informs businesses about potential hazards but also equips them with actionable insights, ultimately forming a vital part of our strategy to prevent workplace injuries on a significant scale.

As a Lead Computer Vision Engineer at CompScience, there are substantial opportunities for career advancement. With our commitment to professional development and mentorship, you will have the chance to grow your skills and take on additional responsibilities. Your role may evolve into higher leadership positions as you drive the technology efforts within the company. Moreover, the fast-paced nature of our startup ensures that your contributions will be highly visible and valued, paving the way for future opportunities.

When designing and implementing a computer vision algorithm for real-world applications, I first begin with understanding the specific needs and objectives of the application. This includes gathering requirements and analyzing existing datasets. Next, I would leverage recent advancements in ML and DL techniques to draft an algorithm design. Then, I would conduct experiments iteratively, evaluate the performance metrics, and optimize the algorithm to meet business objectives effectively.

In my previous role, I had the opportunity to mentor junior engineers by guiding them through the project life cycle of a significant computer vision initiative. I aimed to foster an environment of open communication, regularly scheduled one-on-one sessions, and code review practices that promoted their learning. I provided constructive feedback and resources, which helped enhance their skills and confidence in implementing complex algorithms.

To optimize the performance of computer vision algorithms, I adopt a systematic approach. Initially, I focus on data quality and preprocessing techniques to ensure that the data aligns with the algorithm’s requirements. Then, I explore various hyperparameter tuning methodologies and leverage cross-validation practices to enhance the model’s performance. Lastly, I analyze results using specified metrics and incorporate feedback to continuously refine the algorithm.

To keep up-to-date with the latest trends in computer vision, I regularly participate in industry conferences, read research papers, and engage with online communities. Additionally, I follow leading AI researchers on social media and subscribe to newsletters from reputable organizations. This proactive approach allows me to stay informed about new methodologies and technologies that can inspire my work and inform my strategies as a Lead Computer Vision Engineer.

In my experience, I've extensively worked in cloud environments, particularly AWS, to deploy my computer vision applications. Utilizing AWS tools like S3 for data storage and EC2 for scalable compute resources, I have been able to streamline the deployment process. Moreover, I've implemented AWS Lambda for real-time processing and integrated services like SageMaker for model training and optimization, ensuring that our solutions are both robust and efficient.

To assess risk behaviors using LLMs, I would first ensure that the training dataset captures a wide range of behavioral examples related to workplace safety. Then, I would train the LLM to recognize contextual cues and patterns that indicate potential risks in the dataset. Following this, I’d implement a feedback loop where the model can be continuously refined based on new incidents or behaviors observed, enhancing accuracy and reliability in detecting risk.

A challenging computer vision project I led involved developing an object detection system for an outdoor environment where lighting conditions varied significantly. To overcome this challenge, I implemented data augmentation techniques to create a more robust dataset that included different lighting variations. Additionally, I collaborated closely with my team to iterate on the algorithm and optimize it for real-time processing, ensuring a successful deployment despite the initially daunting conditions.

Object detection focuses on identifying and localizing objects within an image, assigning labels to detected entities, while activity detection goes a step further by observing the trajectories and interactions of these entities over time to understand actions or behaviors. As a Lead Computer Vision Engineer, it’s essential to develop algorithms for both tasks, as they complement each other in understanding complex scenes and improving workplace safety through comprehensive analysis.

Data preprocessing is crucial when developing computer vision models as it directly impacts model performance and accuracy. The steps typically involve resizing images, normalizing pixel values, applying filters to reduce noise, and employing techniques such as image augmentation to expand the training set. All these processes ensure that the model can generalize well and perform effectively across varied inputs in real-world conditions.

To ensure computer vision solutions are user-friendly for non-technical stakeholders, I focus on clear communication and visualizations tailored to their understanding. After developing the algorithm, I create intuitive dashboards and reports that simplify the presentation of data and insights. Additionally, conducting workshops or training sessions can bridge the gap, empowering stakeholders to engage confidently with the technology and its benefits for workplace safety.