דרושים » תוכנה » Computer Vision Researcher-Video Restoration

We are looking for an experienced Senior Algorithm Engineer with expertise in Computer Vision to join our AI team. In this role, you will design, develop, and optimize state-of-the-art vision-based algorithms that power our AI-driven solutions. You will work with large-scale real-world data, contribute to cutting-edge research, and play a key role in bringing advanced AI capabilities to millions of users worldwide.
Responsibilities:
Design, develop, and optimize Computer Vision and Deep Learning algorithms for real-time AI-driven applications.
Work with large-scale visual datasets to train, test, and validate models.
Research and implement state-of-the-art machine learning techniques, including object detection, image segmentation, scene recognition, and 3D processing.
Enhance the efficiency, accuracy, and robustness of existing vision-based models.
Collaborate with software engineers, data scientists, and product teams to integrate algorithms into AI-powered platform.
Stay up to date with the latest advancements in Computer Vision, AI, and Generative Models.

Were looking for a passionate and experienced Computer Vision Algorithms Engineer to join our applied algorithms team and help shape the future of Digital Twin technology. In this role, you will research, design, implement, and optimize advanced machine learning and computer vision models to process and extract insights from data captured by our autonomous drone hives and other imaging systems.

Your work will go from research to real-world impact, delivering robust, production-ready solutions that power mission-critical operations for global enterprises across telecom, wind, and solar. Youll collaborate closely with cross-functional teams to ensure that the algorithms you build are not only accurate and scalable but also aligned with mission of enabling enterprises to digitize smarter, faster, and safer.

Job Description:

Develop algorithms to solve challenging computer vision problems, reducing customers manual workfrom survey analysis to optimal drone flight planning.
Combine methods from deep learning, object detection, segmentation, classification, photogrammetry, 3D mapping, geometry, image processing, and optimization.
Conduct research and feasibility studies, define algorithm requirements, and integrate solutions into the product.
Bring solutions from theory to production, collaborating with cross-functional teams (Product, R&D, Operations) to align on goals and deliverables.
Take ownership of integrating AI capabilities into production and iterating based on real-world feedback.
Work with an agile team of experts working in cutting-edge software development methodologies, to deliver quality software to our customers.

We are looking for a talented Algorithm Engineer to join our Cognitive AI team. In this role, you will design, build, and optimize LLM-based agents that power real-time problem solving. You will be hands-on in Python, research-driven, and passionate about creating intelligent, scalable AI solutions that make a real impact.
Responsibilities:
Design, develop, and optimize LLM-based agents for real-world production use cases.
Build intelligent agent frameworks that orchestrate reasoning across tools, APIs, and knowledge bases.
Deploy and maintain LLM applications in production, ensuring performance, scalability, and reliability.
Research new approaches in LLMs and agent architectures, and integrate cutting-edge techniques into production systems.
Collaborate with cross-functional teams (product, engineering, data) to translate business needs into AI solutions.
Continuously monitor and optimize deployed models, diagnosing and resolving bottlenecks.
Think creatively about complex problems and deliver technological Innovation

We are seeking a skilled ML Engineer to design, build, and deploy advanced ML solutions that drive analytics, anomaly detection, and data classification across enterprise-scale environments.
This role will focus on developing and optimizing models, including LLMs and prompt engineering pipelines, and ensuring seamless integration into production workflows spanning cloud-native, on-premises, and Databricks ecosystems.
Key Responsibilities :
Design, build, and deploy ML models for user behavior analytics, anomaly detection, and data classification across enterprise environments.
Collaborate with data scientists, software and data engineers to integrate ML models into production pipelines, cloud-native environments, on-premises, and Databricks workflows.
Develop, fine-tune, and evaluate LLMs and prompt engineering solutions for data classification, labeling, and threat analysis features.
Optimize models using techniques like distillation, quantization, and efficient data structures to boost performance and lower resource cost.
Build high-performance data inputs using embeddings, vector databases, and distributed training frameworks.
Manage model lifecycle and performance via MLOps best practices: monitoring, retraining, and deploying updates.
Partner with data scientists, cybersecurity researchers and product teams to evaluate and refine ML-driven capabilities.
Conduct experiments and benchmark results in fast-paced, data-intensive environments.

We are looking for a ML Engineer.
We work in a flexible, hybrid model, so you can choose the home-office balance that works best for you.
Responsibilities:
Design, build, and deploy ML models for user behavior analytics, anomaly detection, and data classification across enterprise environments.
Collaborate with data scientists, software and data engineers to integrate ML models into production pipelines, cloud-native environments, on-premises, and Databricks workflows.
Develop, fine-tune, and evaluate LLMs and prompt engineering solutions for data classification, labeling, and threat analysis features.
Optimize models using techniques like distillation, quantization, and efficient data structures to boost performance and lower resource cost.
Build high-performance data inputs using embeddings, vector databases, and distributed training frameworks.
Manage model lifecycle and performance via MLOps best practices: monitoring, retraining, and deploying updates.
Partner with data scientists, cybersecurity researchers and product teams to evaluate and refine ML-driven capabilities.
Conduct experiments and benchmark results in fast-paced, data-intensive environments.