- ML Engineer
- Seattle, WA, USA
- Member since April 5, 2019
Bio
Manuela specializes in taking machine learning models from prototype to production at scale. With expertise in both ML algorithms and software engineering, she builds robust ML pipelines that deliver business value. Her focus on MLOps practices ensures reliable, maintainable systems that serve millions of predictions daily.
Portfolio
Python, TensorFlow, Collaborative Filtering, Real-time Serving, A/B Testing
Scikit-learn, XGBoost, Feature Engineering, Model Monitoring, AWS
PyTorch, Time Series, Anomaly Detection, Edge Deployment, IoT Integration
Experience
Availability
Full-time
Preferred Environment
Cloud-native, Remote
The most amazing…
…model I deployed serves 50M predictions/day with 99.9% uptime at Amazon.
Senior ML Engineer Amazon Web Services
2022 – 2025- Built production ML pipelines serving 50M+ daily predictions with sub-100ms latency.
- Developed automated model retraining system reducing manual work by 80%.
- Implemented feature store and model registry improving team productivity by 40%.
- Led MLOps best practices adoption across 5 product teams.
Technologies: Python, TensorFlow, PyTorch, AWS SageMaker, Lambda, S3, DynamoDB, Docker, Kubernetes, MLflow, Airflow
ML Engineer Spotify
2020 – 2022- Developed recommendation models increasing user engagement by 25%.
- Built real-time feature engineering pipeline processing 100M+ events per day.
- Implemented A/B testing framework for ML model experiments.
- Optimized model serving infrastructure reducing costs by 40%.
Technologies: Python, Scikit-learn, XGBoost, Spark, Kafka, PostgreSQL, Redis, Docker, Kubernetes, GCP
Data Scientist / ML Engineer Fintech Startup
2018 – 2020- Built fraud detection system saving company $5M annually.
- Developed credit scoring models improving approval rates by 15%.
- Created data pipelines for feature engineering and model training.
Technologies: Python, Scikit-learn, Pandas, SQL, AWS, Docker, Flask, PostgreSQL
2014 – 2018
Bachelor of Science in Computer Science & Statistics
University of Washington – Seattle, USA
Machine Learning
Supervised Learning, Unsupervised Learning, Deep Learning, Ensemble Methods, Time Series, Recommendation Systems, Feature Engineering
Languages
Python, SQL, R, Scala, Java
ML Frameworks
TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM, CatBoost, Keras
Data Processing
Pandas, NumPy, Spark, Dask, Polars, Apache Airflow
MLOps
MLflow, Kubeflow, Weights & Biases, Model Registry, Feature Store, Model Monitoring, A/B Testing
Cloud & Infrastructure
AWS SageMaker, AWS Lambda, GCP Vertex AI, Docker, Kubernetes, Terraform, CI/CD
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