Machine Learning Research Engineer

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".

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 behaviour and content creation, translating raw data insights into new predictive modelling 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 applications.
• 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, regression, classification, and optimization techniques.
• Solid engineering fundamentals; comfortable writing production‑level Python and understanding data structures, algorithms, and distributed system design.
• Deep proficiency in Python and the modern ML stack, with hands‑on experience using libraries like Pandas, NumPy, Scikit‑learn, and deep learning frameworks (PyTorch, TensorFlow).
• Gradient debugging expertise in PyTorch or JAX, with experience in distributed training (e.g., DDP, FSDP) and debugging complex gradient issues.
• Applied research ability: read, implement, and improve upon the latest academic papers (NeurIPS, ICML, CVPR) and reproduce results.
• Track record of end‑to‑end ML delivery, from exploratory data analysis and feature engineering to training, validation, and deploying models in production.
• Experience with large‑scale systems, designing resilient architectures that handle vast datasets and high‑throughput inference requests.
• Strong engineering mindset, valuing code quality, testing, modularity, and maintainability as highly as model accuracy.

Education + Experience

• Option A: Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, or related field plus ~3+ years of professional ML engineering experience.
• Option B: No formal degree, ~6+ years of industry experience demonstrating equivalent proficiency in building and shipping ML systems.

What's in it for you

• Competitive equity package
• Health insurance for you and your family
• Corporate pension plan
• Lunch, snacks and drinks provided in the office
• Wellbeing benefit and WFH equipment allowance
• Annual learning and development allowance to grow your skills and career
• Opportunity to work for a globally diverse team

Multi Location: Amsterdam / Berlin / Yerevan / London