Fusemachines is seeking a Senior Machine Learning Engineer to architect, build, and deploy high-performance machine learning systems that power our technology stack. You will work across the entire ML lifecycle—from processing massive volumes of data to developing and deploying low-latency models. We are on 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) 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 handle high-cardinality categorical variables safely.
- Train, tune, and scale supervised learning models using 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, modular production code and transition models from research to high-performance serving environments using technologies like ONNX and TensorRT.
- Design and maintain robust MLOps pipelines for automated model retraining, versioning, shadow deployments, and CI/CD.
- 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 other experimentation frameworks.
What We're Looking For
- Deep expertise in applied machine learning.
- Production-grade software engineering skills.
- A strong hybrid skill set combining ML theory with engineering execution.
Technical Stack
- Languages & Query: SQL, Python
- Distributed Computing: Spark, Ray
- ML Models: XGBoost, LightGBM, CatBoost, Factorization Machines
- Deep Learning: PyTorch, TensorFlow
- Productionization: ONNX, TensorRT
Work Mode
This is a remote position.
