Senior Machine Learning Engineer

Job summary

The Senior Machine Learning Engineer will architect, build, and deploy high-performance machine learning systems across the entire ML lifecycle. Responsibilities include scaling data pipelines, developing deep learning models, and maintaining production-grade MLOps infrastructure.

Job details

About Fusemachines Fusemachines is a leading AI strategy, talent, and education services provider. Founded by Sameer Maskey Ph.D., Adjunct Associate Professor at Columbia University, Fusemachines has a core mission of democratizing AI. With a presence in 4 countries (Nepal, the United States, Canada, and the Dominican Republic) and more than 450 full-time employees, Fusemachines brings global AI expertise to transform companies worldwide. Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government. Fusemachines continues to actively pursue the 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. Type: Remote, Full-time Role Overview We’re hiring a Senior Machine Learning Engineer to architect, build, and deploy high-performance machine learning systems that power technology stack. You will work across the entire ML lifecycle—from processing massive volumes of data to developing and deploying low-latency models. You must possess a strong hybrid skill set: deep expertise in applied machine learning combined with production-grade software engineering skills. You will not just build models in notebooks; you will write scalable, production-ready code, design real-time inference APIs, and ensure your systems meet strict latency and high-throughput requirements. Key Responsibilities Scale Data Engineering & Feature Pipelines 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. Core ML & Deep Learning Development 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. Productionization, MLOps, & System Engineering 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. Evaluation & Experimentation 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. Success in This Role Looks Like You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency). Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring. Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly. Required Qualifications 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. Nice-to-Have 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). Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.

What you’ll do

The Senior Machine Learning Engineer will architect, build, and deploy high-performance machine learning systems across the entire ML lifecycle. Responsibilities include scaling data pipelines, developing deep learning models, and maintaining production-grade MLOps infrastructure.

Requirements

Candidates must have 5-8+ years of experience in machine learning or software engineering with strong proficiency in Python and distributed computing frameworks. A deep understanding of production-grade ML systems, including model serving and real-time inference, is required.

Listed skills

• SQL · Preferred
• CI/CD · Preferred
• Machine learning · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Machine Learning
• Python
• SQL
• PyTorch
• TensorFlow
• Apache Spark
• Distributed Computing
• MLOps
• Data Engineering
• Software Engineering
• XGBoost
• LightGBM
• CatBoost
• CI/CD
• System Architecture
• Statistics
• Business Metrics
• MarTech Solutions
• Operational Efficiency
• Pipelines
• MLOps (Machine Learning Operations)
• Generative Artificial Intelligence
• Workflow Management
• CatBoost (Machine Learning Library)
• Go (Programming Language)
• Data Lineage
• Gradient Boosting
• Digital.ai
• Rust (Programming Language)
• Open Neural Network Exchange (ONNX)
• Java (Programming Language)
• Research
• Application Programming Interface (API)
• Artificial Intelligence
• Applications Of Artificial Intelligence
• Algorithm Design
• Systems Engineering
• Artificial Neural Networks
• Big Data
• C++ (Programming Language)
• Communication
• Cost Reduction
• Data Structures
• Experimentation
• Scalability
• Statistical Hypothesis Testing
• Python (Programming Language)
• Key Performance Indicators (KPIs)
• Object-Oriented Programming (OOP)
• Production Engineering

Job areas

• Technology
• Software
• Data & Analytics
• Engineering
• Machine Learning Engineer
• Software Developers
• Computer and Information Research Scientists

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