Machine Learning Engineer

Responsibilities

Design, develop and maintain automated machine learning pipelines that streamline data processing, model training, evaluation and deployment.
Research, evaluate and implement deep learning architectures for computer vision tasks such as object detection, segmentation, tracking and text recognition.
Collect, clean, label and manage large-scale image and video datasets, and set up labeling workflows with clear quality checks.
Design and run experiments, evaluate model performance with appropriate metrics (precision, recall, mAP) and iteratively improve accuracy, speed and reliability.
Optimize models for real-time inference on servers and edge devices using techniques such as quantization, pruning and TensorRT or ONNX conversion.
Monitor models in production, detect accuracy drops caused by changing conditions such as lighting, weather and camera angles, and retrain when needed.

Comfortable writing code in Python.
Knowledge of frameworks and libraries such as PyTorch, Keras, scikit-learn and OpenCV.
Experience preparing datasets and training and evaluating models. Coursework or personal projects count.
Understanding of machine learning fundamentals such as loss functions, overfitting, regularization and model evaluation.
Have used Linux, Git and Docker.
A bachelor's degree or higher in Computer Science, Mathematics, Artificial Intelligence or a related field is an advantage.

Experience with detection frameworks such as YOLO, Detectron2 or MMDetection.
Experience with MLOps tools such as MLflow, Weights & Biases or DVC.
Experience deploying models on edge devices such as NVIDIA Jetson.
Published research, Kaggle results or open-source contributions in machine learning.