Machine Learning Engineer
Systems, Risk and Trust
Confirmed live in the last 24 hours
Square

10,001+ employees

Digital payment processor
Company Overview
Square's mission is to ensure that all businesses are able to participate and thrive in the economy. The company is building infrastrucutre for online payments.
Consulting
Energy
Social Impact
Financial Services
Consumer Goods

Company Stage

N/A

Total Funding

$601.2M

Founded

2009

Headquarters

Oakland, California

Growth & Insights
Headcount

6 month growth

29%

1 year growth

13%

2 year growth

18%
Locations
San Francisco, CA, USA • Remote in USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Kubernetes
Microsoft Azure
Python
Tensorflow
Data Structures & Algorithms
Pytorch
Docker
AWS
Google Cloud Platform
CategoriesNew
AI & Machine Learning
Software Engineering
Requirements
  • 5+ years of experience in designing and building deep learning capable ML pipelines and infrastructure
  • 8+ years of experience building software platforms
  • Proficiency in Python and experience with machine learning libraries, such as scikit-learn, TensorFlow, or PyTorch
  • Strong software engineering skills, including knowledge of data structures, algorithms, and object-oriented programming principles
  • Experience with cloud-based infrastructure and services, such as AWS, GCP, or Azure, and related machine learning tools and services
  • Familiarity with containerization and orchestration technologies, such as Docker and Kubernetes
  • Experience in dealing with sensitive/private data, both structured and unstructured
  • Experience with advanced techniques like irregular sequence modeling, uncertainty quantification, graph neural networks, homomorphic encryption, active learning systems, is a big plus
  • Strong problem-solving, analytical, and critical thinking skills
  • Entrepreneurial and have demonstrated ability to handle ambiguity
  • Excellent problem-solving, analytical, and communication skills
Responsibilities
  • Collaborate with machine learning modelers and other stakeholders to design and build a robust, scalable, and user-friendly model development infrastructure
  • Implement and maintain machine learning pipelines, including data preprocessing, feature engineering, model training, validation, and deployment
  • Integrate machine learning models with production systems, ensuring seamless and efficient model deployment and monitoring
  • Develop and maintain tools and libraries to facilitate rapid experimentation, model development, and evaluation for the machine learning team
  • Implement best practices for version control, code review, testing, and documentation, fostering a culture of high-quality software development
  • Stay current with the latest tools, technologies, and best practices in machine learning engineering and cloud-based infrastructure, and drive continuous improvement within the team
  • Monitor, troubleshoot, and optimize the performance of machine learning models and related infrastructure
Desired Qualifications
  • Experience with advanced techniques like irregular sequence modeling, uncertainty quantification, graph neural networks, homomorphic encryption, active learning systems
  • Entrepreneurial mindset and ability to handle ambiguity