Svitla - Machine Learning/MLOps Engineer
4 weeks ago
About the Role : We are seeking an experienced Machine Learning & MLOps Engineer to design, develop, and optimize pipelines and infrastructure that support large-scale ML model development and deployment. The ideal candidate will work across data, ML, and engineering teams to ensure robust data workflows, reliable automation, and scalable, production-grade ML systems. This role requires strong technical expertise, hands-on development skills in modern C++, and experience working with large multimodal datasets. Key Responsibilities : - Develop, automate, and optimize data extraction and labeling pipelines for scalable ML workflows. - Design, implement, and refine metrics pipelines, and build monitoring dashboards to track perception system performance in production. - Build and maintain ETL pipelines and ML infrastructure for training, validating, and deploying ML models at scale. - Develop interfaces and infrastructure to collaborate with third-party data labeling partners. - Ensure reproducibility, traceability, observability, and reliability of all perception and ML workflows. - Work closely with cross-functional teams to translate vague or high-level requirements into fully implemented solutions. - Support integration of ML models into production-grade C++ software systems. Required Skills & Abilities : - 5+ years of experience in modern C++ software design and development. - Strong hands-on experience with large multimodal datasets and dataset curation workflows. - Proven ability to architect and implement end-to-end ML solutions, including model training, validation, deployment, and monitoring. - Experience with ML deployment in production environments involving C++ software stacks. - Experience with ETL pipelines, data workflow frameworks, or cloud-native MLOps tools (e.g., MLflow, Argo Workflows, dashboards). - Strong analytical, problem-solving, and debugging skills. Excellent communication skills with a collaborative, self-driven approach.Desirable Skills : - Experience in robotics, autonomous driving, or perception systems. - Hands-on experience implementing end-to-end test frameworks, metrics pipelines, and performance dashboards. - Experience building analytics, visualization, and insight tools for autonomous driving datasets. - Familiarity with Docker, Kubernetes, and containerized ML deployments..- Experience in building 3D Visualization tools for LiDAR point cloud data. (ref:hirist.tech)
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