Machine Learning Engineer

Job Summary

We are looking for a Machine Learning Engineer to design and deploy advanced perception systems that interpret data from external sensors such as cameras and spatial sensing technologies. This role focuses on building intelligent systems capable of understanding real-world environments in real time.

You will work on cutting-edge machine learning and computer vision solutions that enable environmental awareness, object detection, and situational understanding for complex, dynamic environments.

Key Responsibilities

• Develop and deploy machine learning models for real-time perception using sensor data (e.g., cameras, radar-like signals, or other spatial inputs).
• Build computer vision systems for object detection, tracking, and scene understanding.
• Process large-scale datasets to improve model accuracy, robustness, and generalization.
• Implement multi-sensor data fusion techniques to enhance environmental understanding.
• Optimize models for real-time inference with low latency and high reliability.
• Deploy models to edge or embedded systems with hardware constraints.
• Collaborate with cross-functional teams including hardware, software, and data engineering.
• Continuously evaluate and improve model performance under diverse real-world conditions.
• Stay up to date with advancements in machine learning, perception, and AI research.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Robotics, or related field.
• 2–5 years of experience in machine learning, computer vision, or related areas.
• Strong programming skills in Python and/or C++.
• Hands-on experience with deep learning frameworks (PyTorch, TensorFlow).
• Solid understanding of computer vision techniques (object detection, segmentation, tracking).
• Experience working with sensor-generated or image-based data.
• Strong foundation in mathematics (linear algebra, probability, optimization).

Preferred Qualifications

• Experience with real-time perception systems or spatial intelligence.
• Knowledge of sensor fusion techniques (e.g., Kalman filters, multimodal models).
• Familiarity with 3D perception and spatial data processing.
• Experience with edge computing or embedded AI systems.
• Familiarity with ROS (Robot Operating System) or similar frameworks.
• Experience managing large datasets and annotation pipelines.
• Understanding of safety-critical or high-reliability systems.

Key Skills

• Computer Vision & Deep Learning
• Sensor Data Processing & Fusion
• Real-time System Optimization
• Analytical Thinking & Problem Solving
• Cross-disciplinary Collaboration