Fusemachines is hiring a Senior Machine Learning Engineer to architect, build, and deploy high-performance machine learning systems that power our technology stack. In this role, you'll work across the entire ML lifecycle, processing massive volumes of data and deploying low-latency models. You'll join a team with a mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.
What You'll Do
- Process and extract features from massive, highly sparse datasets (terabytes/petabytes of bidstream and user event data) using SQL, Python, and distributed computing frameworks like Spark and Ray.
- Architect offline and online feature pipelines, managing real-time feature computation and low-latency feature stores to ensure zero online/offline skew.
- Perform rigorous missingness analysis, leakage checks, and safely handle high-cardinality categorical variables.
- Train, tune, and scale supervised learning models, utilizing advanced gradient boosting (XGBoost, LightGBM, CatBoost) and Factorization Machines.
- Design and implement Deep Learning architectures for structured and recommendation data using PyTorch or TensorFlow.
- Apply rigorous tabular modeling practices, including meticulous leakage prevention, class imbalance strategies, and robust cross-validation on time-split data.
- Write clean, object-oriented, and modular production code to transition models from research to high-performance serving environments, packaging with tools like ONNX and TensorRT.
- Design and maintain robust MLOps pipelines for automated model retraining, versioning, shadow deployments, and CI/CD for machine learning.
- Monitor production models for data drift, concept drift, and performance degradation in real-time, implementing automated alerting and fallback mechanisms.
- Design rigorous A/B and multivariate tests to evaluate model performance and business impact.
What We're Looking For
- Deep expertise in applied machine learning combined with production-grade software engineering skills.
- Proven experience architecting, building, and deploying machine learning systems at scale.
- Strong proficiency in scaling data engineering and feature pipelines for massive, sparse datasets.
- Expert-level knowledge in training, tuning, and scaling supervised models and deep learning architectures.
- Demonstrated ability to productionize models with rigorous MLOps practices and real-time monitoring.
- Strong analytical skills for evaluation, experimentation, and preventing data leakage.
Technical Stack
- SQL, Python
- Spark, Ray
- XGBoost, LightGBM, CatBoost
- Factorization Machines
- PyTorch, TensorFlow
- ONNX, TensorRT
Work Mode
This role is open to candidates in a global work mode.
