Machine Learning Research Engineer
Miro
full-time
Posted on:
Location Type: Remote
Location: Denmark
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About the role
- 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 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.
Requirements
- Strong foundation in ML theory and statistics, including hypothesis testing, probability distributions, regression, classification, and optimization techniques.
- Solid engineering fundamentals. You are comfortable writing production-level Python and have a deep understanding of 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).
- Expertise in PyTorch or JAX, including experience with distributed training (e.g., DDP, FSDP) and debugging complex gradient issues.
- Ability to read, implement, and improve upon the latest academic papers (NeurIPS, ICML, CVPR). You don't just use libraries; you understand the math underneath them and can reproduce results in peer-reviewed papers.
- Track record of end-to-end ML delivery, from exploratory data analysis (EDA) and feature engineering to training, validation, and deploying models in a production environment.
- Experience with large-scale systems, capable of designing resilient architectures that handle vast datasets and high-throughput inference requests.
- Strong engineering mindset, valuing code quality, testing, modularity, and maintainability just as highly as model accuracy.
Benefits
- 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
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningdeep learningnatural language processingcomputer visionPythonPandasNumPyScikit-learnPyTorchTensorFlow
Soft Skills
collaborationproblem-solvingcommunicationcritical thinkingadaptabilityinnovationattention to detailanalytical thinkingproject managementcross-functional teamwork