AI Computer Vision Engineer Jobs

Software Engineer, Computer Vision and Deep Learning Developing new computer vision algorithms with founders in C/C++ and Python for solving challenging real-world problems, coming up with large scale data collection techniques for training Deep Neural Nets, driving the development of new algorithms that dramatically improve existing methods, researching and maintaining state-of-the-art ML/CV algorithms that can analyze images, and coding full-stack building products from end to end. Lead the research, development, and deployment of state-of-the-art deep learning models for perception of urban scenes in production environments. Architect and optimize complex machine learning systems for scalability, efficiency, and robustness, utilizing cloud-native technologies. Develop and implement training techniques such as data augmentation or model distillation pipelines to improve model robustness under diverse urban and environmental conditions. Explore and integrate novel multi-modal foundational AI models, including large Vision‑Language Models (VLMs), and develop strategies for their effective fine‑tuning and adaptation to specific domain challenges. Drive innovation in model compression, quantization, and efficient inference techniques to optimize performance for both cloud and edge device deployments. Collaborate with cross-functional teams to define machine learning roadmaps, evaluate new technologies, and contribute to the overall technical strategy. Conduct research, and evaluate emerging deep learning techniques applicable to perception and intelligent mobility. Senior Software Engineer, Computer Vision Design, build, implement and optimize multi‑stage computer vision pipelines that span segmentation, object detection, multimodal LLM/LVM extraction, machine‑readable code decoding, and multi‑source reconciliation. Train or fine‑tune detection models on custom medical supply datasets. Build and own dataset strategy including leveraging augmentation and synthetic data generation to improve training and testing datasets when data is scarce. Monitor and improve pipeline accuracy by instrumenting field‑level metrics, diagnosing failure modes, and systematically improving precision and recall through model iteration and preprocessing optimization. Design persistence schemas and audit data models to ensure every data extraction is independently reviewable. Maintain and extend asynchronous Python backend services that provide pipeline results to downstream clinical workflows. Design and implement algorithms for 3D point cloud processing, object detection, and segmentation. Enhance and optimize SLAM pipelines for real‑time mobile and static environments. Work on camera‑LiDAR calibration and multi‑sensor data alignment. Apply deep learning techniques to 2D, 3D, and spatial data. Integrate and optimize tools such as Open3D and 2D+3D inference models into existing systems. Optimize computer vision models and pipelines for NVIDIA GPUs. Perform profiling and performance optimization to improve latency and accuracy. Collaborate with cross‑functional teams across Pakistan and Hong Kong to deliver product features. Participate in R&D to evaluate new computer vision and robotics techniques. Ensure robustness and reliability of computer vision systems in real‑world conditions. Support and guide junior engineers through reviews and technical discussions. The Computer Vision Engineer will deliver hands‑on computer vision work and architect technical solutions for complex project requirements. They will lead the technical delivery of computer vision projects and provide expert guidance to multidisciplinary teams throughout the development lifecycle. The role includes contributing expert computer vision insight to bids and identifying opportunities to integrate advanced visual intelligence into customer solutions. The engineer will stay at the forefront of the field by mastering State‑of‑the‑Art developments and sharing best practices across the business unit. They will represent the organization internally and externally as a subject matter expert in computer vision, partner with leadership to define the technical strategy for computer vision work, take ownership of capability development within the Defence domain, and mentor and develop team members interested in computer vision, fostering a continuous learning and technical excellence environment. Senior Computer Vision Engineer (Autonomous Driving) As a Senior Computer Vision Engineer at 42dot, responsibilities include researching and developing 3D computer vision and machine learning algorithms for autonomous driving technology, performing 3D shape modeling and processing, implementing object pose estimation and tracking algorithms, developing efficient and scalable vision solutions, exploring the intersection of vision and robotics, working on low‑level and physics‑based vision algorithms, conducting self‑supervised representation learning from large‑scale unlabeled scene data, and creating world models and closed‑loop simulation for autonomous driving. Engineer an advanced Generative AI pipeline capable of transforming the context of existing datasets, including shifting time‑of‑day, changing seasons, or altering biomes and weather systems while preserving small target objects like drones. Take ownership of a multi‑pass diffusion pipeline to adapt scene contexts and maximize physical realism. Improve custom masking and high‑resolution depth‑patching algorithms to anchor small objects in 3D space, eliminating artifacts. Generate large‑scale augmented datasets and quantify their impact on downstream model performance, designing experiments to measure the effect of synthetic data on the accuracy, recall, and robustness of object detectors when tested against real‑world edge cases. Conduct research on state‑of‑the‑art Computer Vision methodologies and participate in creating and curating training and validation datasets. Perform statistical analyses and develop visualization tools to ensure data quality. Build and refine training pipelines and metrics to enhance model performance. Develop and optimize Computer Vision algorithms for multiple robotics/aerospace projects. Implement ML/CV models into production‑ready environments, ensuring seamless integration with Harmattan AI’s systems and conducting rigorous code reviews. Test algorithms in real‑world environments and develop monitoring tools to track model performance and continuously improve deployed solutions. Work closely with software and simulation teams to align development with system requirements and communicate findings effectively to stakeholders. The Computer Vision Engineer is responsible for developing the front‑end of the visual inertial odometry (VIO) algorithmic stack, including matching between frames and stereo pairs, calibration of camera intrinsic and extrinsic parameters, and detection of obstruction. They will implement and optimize the algorithmic stack for embedded platforms, conduct testing, validation, and monitoring of algorithms in simulation and real‑world environments, and develop inspection and monitoring tools. The role also involves cross‑team collaboration, working closely with system engineers, optical engineers, and software engineers, and effectively communicating findings to stakeholders. What skills are required for AI Computer Vision Engineer Jobs? Essential skills for AI Computer Vision Engineers include strong proficiency in Python programming and deep understanding of computer vision libraries like OpenCV and scikit‑image. They need expertise in deep learning frameworks such as PyTorch and TensorFlow, plus specialized knowledge of CNNs and advanced architectures like YOLO, R‑CNN, and U‑Net. Image processing techniques, data pipeline management, and model optimization capabilities are crucial. Engineers must demonstrate problem‑solving abilities when addressing complex visual challenges and know how to evaluate model performance using appropriate metrics. Experience with Docker containerization, cloud deployment platforms, and version control systems rounds out their technical toolkit. Strong communication skills help them collaborate effectively across teams. What qualifications are needed for AI Computer Vision Engineer Jobs? AI Computer Vision Engineer positions typically require at least a bachelor's degree in computer science, electrical engineering, or related fields, though many employers prefer candidates with advanced degrees. Most roles expect a minimum of 1 year of computer vision engineering experience. Candidates should have demonstrable knowledge of machine learning algorithms, image processing techniques, and mathematics fundamentals. A strong portfolio featuring computer vision projects is highly valuable, showcasing practical implementation skills. Domain knowledge in specific application areas (autonomous vehicles, medical imaging, retail analytics) can be advantageous for specialized roles. Certifications in deep learning or computer vision frameworks provide additional credibility, especially for early‑career professionals. What is the salary range for AI Computer Vision Engineer Jobs? Compensation for AI Computer Vision Engineers varies based on several key factors. Experience level significantly impacts earnings, with senior roles commanding premium salaries. Geographic location plays a major role, with technology hubs typically offering higher compensation packages. Industry sector also influences pay scales - autonomous vehicle companies and medical imaging firms often provide competitive salaries due to specialized requirements. Technical expertise depth, particularly with cutting‑edge computer vision techniques like advanced CNN architectures or 3D vision, can elevate compensation. Company size and funding stage matter too, with established tech companies generally offering higher base salaries while startups might provide more equity components. #J-18808-Ljbffr