Machine Learning Engineer (Edge AI & Computer Vision)
# Machine Learning Engineer (Edge AI & Computer Vision) **Marvik** · Argentina; Chile; Colombia; Ecuador; El Salvador; Mexico; Paraguay; Peru; Uruguay · `Remote` · `Full Time` 💼 **Уровень роли:** `Senior` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Machine-Learning-Engineer]` `[Computer-Vision-Engineer]` `[Edge-AI-Engineer]` `[AI-Engineer]` `[Deep-Learning-Engineer]` `[Senior-Edge-AI-Engineer]` `[Computer-Vision-and-Machine-Learning-Engineer]` `[Computer-Vision-ML-Engineer]` `[Senior-AI-ML-Engineer]` `[Lead-Machine-Learning-Engineer]` `[ML-Edge-Engineer]` `[Lead-AI-ML-Engineer]` --- ### About the Role Want to work with cutting-edge technologies on long-term, strategic projects that combine Deep Learning, Computer Vision, Edge AI, and IoT ? 🚀🤖🚜 At Marvik , we are leading the next evolution of smart machinery—integrating vision capabilities, real-time image processing, and multi-sensor fusion into physical products operating in real environments. 🌟 What do we offer?: - Challenging, real-world projects: Work on stuff other people only read about—bringing AI out of the cloud and straight into physical, intelligent machinery. - State-of-the-art tech stack: Optimize and deploy cutting-edge Computer Vision and Deep Learning models on Edge devices. - Strategic growth: Join an expanding team with huge potential for long-term ownership, leadership, and professional development. - Great team culture: Excellent work environment full of highly motivated, collaborative, and talented professionals who elevate each other. - Global exposure & On-site experiences: Opportunity to travel internationally to work directly with clients. - Flexible work style: Opportunity to work remotely with global, high-impact clients. 🧑🏻💻 Responsibilities: - Take end-to-end ownership of Machine Learning models, moving them beyond training and ensuring reliable deployment in production on physical hardware. - Design, build, and optimize real-time AI pipelines, integrating foundation models, sensor and application data, and scalable inference workflows. - Optimize inference performance, memory usage, and execution speed for Edge AI platforms (e.g., NVIDIA Jetson, embedded platforms). - Collaborate directly with clients and cross-functional engineering teams, building trust, proposing proactive solutions, and maintaining smooth technical communication. - Debug, monitor, and maintain production models operating continuously under real-world hardware constraints. 🤝 If you have: - Strong experience bringing AI to production: It’s not just about training models—you know how to make them run reliably in real-world environments. - Solid background in AI & Deep Learning , including experience with LLMs, Computer Vision, multimodal models, model optimization, and efficient inference. - Edge AI & Embedded exposure: Experience optimizing models for constrained devices (TensorRT, ONNX, OpenCV, C++, or Python). - Strong Ownership & Soft Skills: Highly collaborative, proactive, independent, and clear in technical communication. A team player who builds trust with clients and peers. - Advanced English level: Excellent verbal and written communication skills to interact directly with international client teams. - Required tools: Python, C++, PyTorch/TensorFlow, OpenCV, Docker, Git. 🦾 It’s a major plus: - Hands-on experience with NVIDIA Jetson , ROS/ROS2, or embedded hardware platforms. - Experience in domains such as Robotics, IoT, Drones, Automotive, or Industrial Machinery . - Knowledge of sensor fusion (IMU, cameras, LiDAR) or OTA (Over-The-Air) updates and Cloud IoT architectures (AWS/Azure IoT). Originally posted on Himalayas
- Machine-Learning-Engineer
- Computer-Vision-Engineer
- Edge-AI-Engineer
- AI-Engineer
- Deep-Learning-Engineer
- Senior-Edge-AI-Engineer
- Computer-Vision-and-Machine-Learning-Engineer
- Computer-Vision-ML-Engineer
- Senior-AI-ML-Engineer
- Lead-Machine-Learning-Engineer
- ML-Edge-Engineer
- Lead-AI-ML-Engineer
Наблюдалась 2026-10-10, впервые 2026-10-10, источник — Himalayas (JSON API).