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Senior Machine Learning Platform Engineer, ML Foundations

4 months ago

Oakland, United States Block Full time

Company Description
Block is one company built from many blocks, all united by the same purpose of economic empowerment. The blocks that form our foundational teams - People, Finance, Counsel, Hardware, Information Security, Platform Infrastructure Engineering, and more - provide support and guidance at the corporate level. They work across business groups and around the globe, spanning time zones and disciplines to develop inclusive People policies, forecast finances, give legal counsel, safeguard systems, nurture new initiatives, and more. Every challenge creates possibilities, and we need different perspectives to see them all. Bring yours to Block.
Job Description
About machine learning
ML is essential to Block's daily operations and long term success. Its usage has grown dramatically over the past few years and is only accelerating. As more teams integrate ML capabilities, so has the need to avoid duplication by providing shared capabilities.
About the team
Machine Learning Foundations (MLF) builds scalable, composable components for ML use cases. We work closely with platform and product teams across the company to amplify their growth by solving complex problems that impact multiple business groups. Block's ML community is too large and moves too quickly for MLF to stay ahead of such a diverse set of needs, so we target a narrower set of use cases that will have an outsized impact on our internal customers.
About the role
We are looking for an experienced engineer to join MLF. While your initial focus will be building self-service tooling for the model lifecycle, particularly model deployments, serving, and monitoring, you will also have opportunities to work across the entire machine learning lifecycle. As a platform engineer, you will work closely with our internal customers to understand their needs and translate those into sustainable software solutions.
We are looking for someone who has experience primarily as a machine learning engineer or modeler, as well as a software engineer, as we operate at the intersection of those two roles. Candidates must demonstrate professional experience with the end-to-end ML lifecycle, and we prefer candidates who also have experience building platforms. Beyond that:
You will

  • Design and build tools and systems that make data scientists happier and more productive
  • Work closely with data science and engineering teams across Block, particularly Cash and Square, to understand their needs and solve their problems
  • Lead architectural and design discussions to ensure our platform is modular, scalable, fault tolerant, and sustainably built
  • Mentor your teammates and assist engineers on other teams who integrate with our platform
  • Leverage your machine learning knowledge by providing insightful feedback
  • Participate in an oncall rotation; maintain exceptional reliability standards while ensuring the team's oncall rotation is sustainable


Qualifications
You have

  • 8+ years of combined experience in software engineering and machine learning engineering
  • 5+ years of experience with the ML lifecycle, such as feature engineering, model training, etc.
  • Strong technical judgment and meaningful experience handling complex technical concepts
  • Experience with large-scale distributed systems built around ML
  • A track record of healthy collaboration with product managers, engineers, and other stakeholders
  • Preferred: experience building machine learning platforms or infrastructure


Qualifications
You have

  • 8+ years of combined experience in software engineering and machine learning engineering
  • 5+ years of experience with the ML lifecycle, such as feature engineering, model training, etc.
  • Strong technical judgment and meaningful experience handling complex technical concepts
  • Experience with large-scale distributed systems built around ML
  • A track record of healthy collaboration with product managers, engineers, and other stakeholders
  • Preferred: experience building machine learning platforms or infrastructure