Machine Learning Technical Lead, Data Engineering - SIML, ISE

In this position, you will work with SIML Data functions and with ML teams to assess data engineering needs tied to shipping ML features. You will partner with and influence the roadmap of teams that build infrastructure blocks that we rely upon (eg storage & labeling platforms), in order to contribute to a best-in-class ML Data Engine. Our team of data engineers will use these systems to support end-to-end data flows tied to collection/annotation/QA operations, deliver high quality data quickly to ML teams, ensure traceability, versioning and lineage of data objects, and enforce compliance to contractual and regulatory obligations.

As a tech lead specialized in data engineering, you are also expected to code and contribute to the stack. You will establish and execute the strategy for our organization’s Machine Learning Data Engine with an initial focus on agile ML Data OPs. This includes identification of infrastructure components and data stack to be used, design and implementation of pipelines between data systems and teams, automation workflows, data visualization and tools, data enrichment and monitoring tools.

• 7 - 10+ years of industry experience as a software engineer, with recent involvement in parts of the ML lifecycle, and a strong understanding of applied machine learning topics
• Proven experience as a tech lead specializing in data engineering/infrastructure
• Experience designing and building large scale data processing systems; keeping up to date with the latest technologies, comfortable performing benchmarks, prototyping and bringing new systems to production
• The know-how to manage complex data projects while establishing and enhancing the right software engineering culture for our team
• Experience in building data pipelines to process large scale datasets, using orchestration frameworks like Airflow, KubeFlow or similar pipeline tools
• Expertise in Python, or another modern programming language
• Proficiency to design and lead a technical roadmap in alignment with R&D cross-functional teams with the capacity to influence other data infrastructure teams, and collaborate with members of our data Ops functions
• Self-starter, able to handle ambiguity, identify risks, troubleshoot, and find the right people and tools to get the job done