Company:
Block
Location: San Francisco
Closing Date: 18/10/2024
Hours: Full Time
Type: Permanent
Job Requirements / Description
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 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 to 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 with our internal customers to understand their needs and translate them into sustainable software solutions.
We are looking for someone who has experience as a machine learning engineer or a software engineer, as we operate at the intersection of those two roles. 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 with data science and engineering teams across Block, 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
Use your machine learning knowledge by providing insightful feedback
Participate in an oncall rotation; maintain reliability standards while ensuring the team's oncall rotation is sustainable
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 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 to 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 with our internal customers to understand their needs and translate them into sustainable software solutions.
We are looking for someone who has experience as a machine learning engineer or a software engineer, as we operate at the intersection of those two roles. 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 with data science and engineering teams across Block, 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
Use your machine learning knowledge by providing insightful feedback
Participate in an oncall rotation; maintain reliability standards while ensuring the team's oncall rotation is sustainable
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