Machine Learning Intern

Job highlights

• Bachelor / Masterin Computer Science,Robotics,Electronics,or related field
• Solid experience in

Role Summary

As a Machine Learning Engineer at ERIC Robotics, you will be responsible for developing and deploying advanced machine learning algorithms with a focus on sensor fusion, real-time inference optimization, and deployment on edge devices . Your role will merge deep learning, 3D perception, and robotics to enhance the intelligence, perception, and decision-making capabilities of our robots. You will also contribute to MLOps workflows, ensuring reproducibility, scalability, and efficient model deployment.

Key Responsibilities

Core ML Sensor Fusion

Develop, train, and optimize deep learning models for robotic perception and decision-making.

Implement sensor fusion pipelines (camera + LiDAR + IMU + other sensors) for robust perception.

Apply Open3D/PCL for 3D point cloud processing and integration into robotic navigation systems.

Optimize model inference using ONNX/TensorRT/CUDA for deployment on edge GPUs (Jetson, Orin, Xavier).

Integrate perception models with control and navigation stacks for real-time robotics applications.

MLOps Experiment Tracking

Implement experiment tracking using MLflow/WB for reproducibility and systematic evaluation.

Develop workflows for cloud-based training, deployment, and monitoring of ML models.

Automate CI/CD pipelines for ML model updates and deployment.

Mathematical Algorithmic Foundation

Apply linear algebra, calculus, and probability in designing ML algorithms and robotics perception systems.

Work on computational optimization problems in real-time robotics scenarios.

General Collaboration

Collaborate with cross-functional teams (robotics, firmware, PLC, cloud, hardware).

Document research, experiments, and deployments for knowledge sharing.

Contribute to innovative problem-solving, logical reasoning, and teamwork communication.

Requirements

Bachelor/Masterin Computer Science, Robotics, Electronics, or related field.

Strong proficiency in Deep Learning frameworks (PyTorch/TensorFlow) .

Solid experience in C++ and Python for ML model development .

Familiarity with ROS/ROS2-based robotic systems .

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