Machine Learning Research Engineer

About the Role

Miro is seeking a Machine Learning Research Engineer to serve as the technical North Star for its Machine Learning organization. Operating as an Individual Contributor, you will drive architectural decisions for the Intelligent Canvas.

What You’ll Do

• Design, train, and deploy production-grade ML models including deep learning, NLP, and computer vision systems.
• Conduct deep research on large datasets to uncover novel user behavior patterns.
• Apply advanced fine tuning strategies such as PEFT and LoRA to adapt foundation models.
• Architect scalable ML pipelines for data processing, feature engineering, training, and evaluation.
• Optimize model performance balancing latency, throughput, and resource use.
• Collaborate cross-functionally to translate business needs into technical ML specifications.
• Champion MLOps by automating deployments, implementing CI/CD for ML, and monitoring model health.
• Stay updated with ML research and evaluate novel algorithms including Transformer architectures and quantization methods.

What You’ll Need

• Strong foundation in ML theory, statistics, and advanced mathematics.
• Production-level Python skills with deep understanding of data structures and algorithms.
• Hands-on experience with modern ML libraries such as Pandas, NumPy, Scikit-learn, PyTorch, and TensorFlow.
• Expertise in gradient debugging using PyTorch or JAX including distributed training techniques.
• Ability to implement and improve upon academic research findings from top-tier conferences.
• Proven experience in full-cycle ML delivery across EDA, feature engineering, and deployment.
• Experience with designing scalable architectures for large datasets and high-throughput inference.
• Strong emphasis on code quality, testing, modularity, and maintainability.

Education & Experience

Option A: Bachelor's or Master's in Computer Science, Mathematics, Statistics, or related field plus ~3+ years of experience. Option B: Equivalent industry experience of ~6+ years.

Benefits & Company Culture

• Competitive equity package
• Health insurance and corporate pension plan
• Office amenities including lunch, snacks, and drinks
• WFH equipment allowance and wellbeing benefits
• Annual learning and development allowance
• Opportunity to work with a globally diverse team

Key skills/competency

• machine learning
• deep learning
• NLP
• computer vision
• Python
• ML pipelines
• distributed training
• MLOps
• research
• architecture