Note: The job is a remote job and is open to candidates in USA. Fusemachines is an AI strategy, talent, and education services provider focused on democratizing artificial intelligence. The company is hiring a Senior Machine Learning Engineer to architect, build, deploy, and monitor high-performance machine learning systems across the full ML lifecycle, including large-scale data processing, model development, production serving, MLOps, and experimentation.
Responsibilities
- Process and extract features from massive, highly sparse datasets (terabytes/petabytes of bidstream and user event data) using SQL, Python, and distributed computing frameworks (e.g., Spark, Ray)
- Architect offline and online feature pipelines. Manage real-time feature computation and low-latency feature stores ensuring zero online/offline skew
- Perform rigorous missingness analysis, leakage checks, and handle high-cardinality categorical variables safely
- 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/recommendation data using PyTorch or TensorFlow
- Apply rigorous tabular modeling practices: meticulous leakage prevention, class imbalance strategies, and robust cross-validation on time-split data
- Write clean, object-oriented, and modular production code. Transition models from Python research environments to high-performance serving environments (packaging with ONNX, TensorRT, etc)
- Design and maintain robust MLOps pipelines: 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 measure the true business incrementality of ML models
- Choose appropriate offline metrics (PR-AUC, normalized Entropy/LogLoss, Calibration, Lift) and bridge them to online business KPIs
Skills
- 5–8+ years of experience as a Machine Learning Engineer or Software Engineer focusing on ML systems, ideally within Ad Tech, MarTech, or high-scale recommendation systems
- Production Engineering Skills: Strong software engineering fundamentals (OOP, data structures, algorithm design). Expert-level Python and strong proficiency in a compiled or high-performance language (e.g., C++, Java, Scala, Go, or Rust)
- ML Systems & Serving: Deep experience deploying machine learning models into highly concurrent, low-latency production environments (APIs, microservices, Triton Inference Server, custom containers)
- Distributed Computing: Hands-on experience with big data processing (Apache Spark, Kafka, Flink) and complex SQL queries
- Core ML & Deep Learning: Proven track record of shipping both tree-based models and neural networks (PyTorch/TensorFlow) to production
- Statistics & Experimentation: Solid grasp of statistics, hypothesis testing, and rigorous A/B experiment design
- Agentic / GenAI Development: Experience designing agentic workflows or utilizing LLMs to automate ad creative generation, campaign copilot tools, or internal ML development workflows (AI-assisted IDEs, code agents)
Benefits
- Remote work arrangement
Company Overview
Company H1B Sponsorship