Machine Learning Research Engineer

Department: Engineering Locations: Copenhagen, London, Remote EMEA Job Requisition ID: JR001665

Table of contents

• What you’ll do
• What you’ll need
• What's in it for you
• About Miro
• Apply

Miro is looking for a Machine Learning Research Engineer to serve as the technical "North Star" for our Machine Learning organization. You will operate as an Individual Contributor, driving the architectural decisions behind the "Intelligent Canvas." Your challenge is unique in the industry: You are not just processing text. You are building models that understand spatial relationships, visual diagrams, and unstructured collaboration. You will research, prototype, and ship novel architectures that combine Large Language Models (LLMs), Computer Vision, and Graph Neural Networks (GNNs) to make Miro the smartest collaboration platform on earth.

What you’ll do

• Design, train, and ship production-grade ML models—including deep learning, NLP, and computer vision systems—that solve complex business problems and power core product features.
• Conduct deep exploratory research on massive datasets to uncover novel patterns in user behavior and content creation, translating raw data insights into new predictive modeling opportunities.
• Apply advanced fine-tuning strategies (e.g., PEFT, LoRA) to adapt state-of-the-art foundation models to specific domain tasks, rigorously experimenting to maximize performance.
• Architect scalable ML pipelines for data processing, feature engineering, training, and evaluation, ensuring high data quality and system reliability.
• Optimize model performance for latency, throughput, and resource utilization, balancing model complexity with production constraints (e.g., overfitting vs. underfitting, compute efficiency).
• Collaborate cross-functionally with data engineers, product managers, and software engineers to translate business requirements into technical ML specifications and integrate models into user-facing applications44.
• Champion MLOps excellence by automating deployment workflows, implementing CI/CD for ML, and establishing robust monitoring for model drift and health.
• Stay at the forefront of ML research, evaluating novel algorithms and techniques (e.g., Transformer architectures, quantization) to drive innovation and technical strategy.

What you’ll need

• Strong foundation in ML theory and statistics, including hypothesis testing, probability distributions,...