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.