Staff Machine Learning Engineer, Risk Signals & Controls

Block is a company focused on building technology to increase access to the global economy. They are seeking a Staff Machine Learning Engineer for the Risk Signals and Controls team to design and maintain data pipelines, enhance modeling capabilities, and collaborate on fraud detection solutions.

Responsibilities:

• Design, build, and maintain data pipelines to ingest third parties and Block data to enhance our feature store and modeling capabilities
• Design elegant ML pipelines and services, prototype new approaches, and productionize solutions at scale
• Build tooling to help maintain our features and models, gaining efficiencies in fraud detection
• Work hand-in-hand with ML Modelers to identify and integrate new data sources, heuristics and models
• Solve challenging technical problems at scale, collaborating with colleagues located across the globe
• Apply ML and engineering best practices to shape how Block develops, tests, and maintains ML-platform solutions
• Ensure data quality and completeness through automated validation, monitoring, and alerting
• Own your solutions from design through to operation: we all share the pager!

Requirements:

• 12+ years of experience in software development and demonstrated technical initiative and leadership on previous machine learning projects
• A proven ability to shape how teams effectively adopt and evolve AI practices; driving adoption of AI-first workflows by coaching leaders, identifying scalable use cases, and embedding quality and accountability in team norms
• Work autonomously in a fast paced, ambiguous and unpredictable environment
• Natural curiosity & eagerness to learn
• Show the ability to take initiative, lead, and drive creative solutions in cross-team contexts
• A strong interest in owning your solutions, maintaining and fixing them as necessary
• Communicate via clear and concise writing to facilitate asynchronous collaboration across multiple time zones
• Reason about complex, distributed systems at high scale
• Proficiency in machine learning techniques, experimental design and data engineering
• Strong programming skills in languages such as Kotlin, Python, TensorFlow, or PyTorch