Tech Stack

Description

As a Machine Learning Research Engineer, you will drive architectural decisions for the intelligent canvas at Miro, building models that understand spatial relationships, visual diagrams, and unstructured collaboration. You'll research, prototype, and ship novel architectures combining LLMs, Computer Vision, and GNNs to make Miro the smartest collaboration platform.

Requirements

• Strong foundation in ML theory and statistics including hypothesis testing, probability distributions, and optimization
• Solid engineering fundamentals with production-level Python and understanding of distributed systems
• Deep proficiency in Python and ML stack with hands-on PyTorch or TensorFlow experience
• Ability to read, implement, and improve upon latest academic papers (NeurIPS, ICML, CVPR)
• Track record of end-to-end ML delivery from EDA to deployment

Responsibilities

• Design, train, and ship production-grade ML models including deep learning, NLP, and computer vision systems
• Conduct deep exploratory research on massive datasets to uncover novel patterns in user behavior and content creation
• Apply advanced fine-tuning strategies (e.g., PEFT, LoRA) to adapt foundation models to domain tasks
• Architect scalable ML pipelines for data processing, feature engineering, training, and evaluation
• Optimize model performance for latency, throughput, and resource utilization