- Company Name
- Miro
- Job Title
- Machine Learning Research Engineer
- Job Description
-
Job title: Machine Learning Research Engineer
Role Summary:
Drive architectural design and delivery of production‑grade ML models that fuse LLMs, computer vision, and graph neural networks for the Intelligent Canvas. Lead end‑to‑end research, prototype, and deploy scalable ML pipelines, ensuring model performance, reliability, and alignment with product goals.
Expectations:
• Independent contributor, setting technical direction within the ML organization.
• Deliver experimental insights into user behavior and content creation at scale.
• Maintain high standards of code quality, testing, and documentation.
Key Responsibilities:
- Design, train, and ship deep learning, NLP, and CV models for core product features.
- Conduct exploratory data analysis on large datasets to discover novel patterns.
- Apply fine‑tuning techniques (PEFT, LoRA) and advanced optimization to foundation models.
- Architect end‑to‑end ML pipelines (data ingestion, feature engineering, training, inference).
- Optimize models for latency, throughput, and resource efficiency in production.
- Collaborate with data engineers, product managers, and software teams to translate requirements into ML specifications and integrate models into user‑facing applications.
- Champion MLOps practices: CI/CD, automated deployments, monitoring for drift and health.
- Evaluate and adopt cutting‑edge ML research (Transformers, quantization, etc.) to inform technical strategy.
Required Skills:
- Strong ML theory and statistics (hypothesis testing, probability, regression, classification, optimization).
- Production‑level Python, deep learning frameworks (PyTorch, TensorFlow), and ML libraries (Pandas, NumPy, Scikit‑learn).
- Expertise in distributed training (DDP, FSDP) and gradient debugging.
- Experience reading and implementing research papers; ability to reproduce and improve results.
- End‑to‑end ML delivery experience: EDA, feature engineering, training, validation, deployment.
- System design for high‑throughput, large‑scale inference.
- Code quality focus: modularity, testing, maintainability.
Required Education & Certifications:
Option A: Bachelor’s or Master’s in Computer Science, Mathematics, Statistics, or related field + ~3+ years professional ML engineering experience.
Option B: No formal degree, but ~6+ years industry experience demonstrating equivalent proficiency in building and shipping ML systems.