Machine Learning Research Engineer

Key Responsibilities: Develop advanced computer vision and state-of-the-art deep learning models to understand software applications and derive user intent. Research and implement ML and deep learning algorithms for production use. Build and maintain infrastructure for AI/ML systems, including model inference, automated (re-)training, monitoring, explainability, and more. Stay updated on cutting-edge AI research from leading labs by reading papers and experimenting with code. Assist AI product managers and business stakeholders in understanding the capabilities and limitations of AI for the products. Fine-tune, retrain, and scale existing model deployments. Conduct necessary ML tests and benchmarks for model validation. Our Ideal candidate: Experience: 4+ years of expertise in machine learning. Deep Learning Expertise: Strong understanding of deep learning principles in both Computer Vision and Natural Language Processing (NLP). LLM Knowledge: Familiarity with LLM architectures like BERT and GPT, with hands-on experience fine-tuning these models for enhanced performance. Model Development: Proven experience in developing deep learning models, such as CNNs and Vision Transformers, for image-based tasks in production systems. Research and Application: Extensive experience in algorithms for image processing, content-based video/image analysis, object detection, segmentation, and tracking. Libraries: Proficiency in machine learning and deep learning libraries like PyTorch and Hugging Face. Technical Skills: Demonstrated ability to implement, improve, debug, and maintain machine learning models. Nice to Have: Strong programming skills in Python. Expertise in optimization and debugging. Familiarity with version control systems such as Git. Self-motivated and responsible, with excellent written and verbal communication skills. Experience in handling and visualizing large datasets and creating performance reports.