Explore high-impact Senior Platform Engineer, ML Data Systems jobs and discover a career at the critical intersection of machine learning, data infrastructure, and software engineering. Professionals in this specialized role are the architects and builders of the robust, scalable data platforms that power modern AI and machine learning initiatives. They move beyond traditional data engineering by focusing specifically on the unique challenges of ML data lifecycle management, ensuring that data scientists and ML engineers have reliable, high-quality, and efficiently accessible data to build, train, and evaluate models. This position is central to operationalizing AI, making it a sought-after and rewarding career path for those passionate about enabling data-driven innovation. A Senior Platform Engineer specializing in ML Data Systems typically shoulders the responsibility of designing, constructing, and maintaining the end-to-end data infrastructure required for machine learning. This involves creating and orchestrating scalable ETL (Extract, Transform, Load) and ELT pipelines that transform raw, often messy, data into clean, structured, and annotated datasets ready for model consumption. They implement systems for data versioning, lineage tracking, and reproducibility, which are crucial for iterative model development. A core part of the role is developing frameworks and tools for dataset management, including processes for human-in-the-loop labeling, quality validation, and integration of feedback data. These engineers ensure data governance, implementing controls for sensitive information and managing data retention policies to maintain security and compliance. Common responsibilities for these professionals include collaborating closely with cross-functional teams of data scientists, ML engineers, and product managers to define data strategy and requirements. They are tasked with optimizing data storage and retrieval patterns on cloud platforms (like AWS, GCP, or Azure) for performance and cost. Automating monitoring and alerting for data pipelines to detect issues like schema drift, data quality degradation, or pipeline failures is also standard. Furthermore, they contribute to MLOps practices by bridging the gap between data preparation and model deployment, often building tools that streamline the journey from experiment to production. Typical skills and requirements for Senior Platform Engineer, ML Data Systems jobs include a strong foundation in software engineering, usually evidenced by 5+ years of experience, with several years focused on data or ML systems. Proficiency in programming languages such as Python, Go, or Java is essential, along with expert-level SQL. Hands-on experience with data pipeline orchestration tools (e.g., Apache Airflow, Dagster, Prefect) and data processing frameworks is expected. Deep familiarity with data versioning tools (like DVC or LakeFS), cloud data warehouses, and storage solutions is crucial. A solid understanding of machine learning concepts, workflows, and the data needs of different model types (especially large language models) is a key differentiator. Success in this role also demands a meticulous attention to detail, a passion for data quality, and strong problem-solving and communication skills to translate complex data infrastructure concepts for diverse stakeholders. For engineers who thrive on building the foundational systems that enable AI breakthroughs, exploring Senior Platform Engineer, ML Data Systems jobs offers a path to a pivotal and future-proof career.
