Software Engineer, Machine Learning Infrastructure

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Written by AI from the job title and company, not by Stripe, so some details may not match this job. The employer's own page has the real description.

Overview

As a Software Engineer, Machine Learning Infrastructure at Stripe, you will be responsible for designing and implementing scalable and efficient machine learning systems that support various applications across the organization. You will collaborate with cross-functional teams to build robust infrastructure, optimize algorithms, and ensure seamless integration of machine learning models into production environments. This role requires a strong foundation in software engineering principles, machine learning techniques, and a commitment to enhancing the performance and reliability of our systems.

Job Responsibilities

• Design, implement, and maintain machine learning infrastructure that supports model training, validation, and deployment.
• Collaborate with data scientists and product teams to understand requirements and provide solutions for integrating ML into applications.
• Optimize and scale machine learning models for high-performance use cases, ensuring reliability and maintainability.
• Develop and enforce best practices for model development, versioning, and lifecycle management.
• Troubleshoot and resolve issues related to machine learning model performance and infrastructure.
• Contribute to open-source projects and reusable frameworks that can enhance the machine learning community at Stripe.

Qualifications

• Bachelor's or Master’s degree in Computer Science, Engineering, or a related field.
• Proven experience in software engineering, particularly in developing scalable systems.
• Strong knowledge of machine learning concepts and algorithms, as well as practical experience in applying them.
• Proficiency in programming languages such as Python, Java, or C++.
• Experience with machine learning frameworks and tools, such as TensorFlow, PyTorch, or Scikit-learn.
• Familiarity with cloud platforms and technologies (e.g., AWS, GCP, Azure) for deploying ML workloads.
• Strong problem-solving skills and the ability to work collaboratively in a fast-paced environment.

Technologies & Tools

This position requires proficiency in various technologies and tools including but not limited to programming frameworks like TensorFlow and PyTorch, cloud services such as AWS or Google Cloud Platform, and version control systems like Git. Familiarity with container orchestration tools such as Kubernetes for deployment management, as well as SQL and NoSQL databases for data management, is also essential in this role.

Ideal Candidates

The ideal candidates for this position will possess a mix of technical skills and personal qualities that allow them to thrive in a collaborative environment. They should be innovative thinkers who are enthusiastic about leveraging technology to solve complex problems. Strong communication skills and the ability to work collaboratively across teams are vital, as is a commitment to continuous learning and improvement in the fast-evolving field of machine learning. Candidates should also demonstrate resilience in facing challenges and adaptability in a dynamic work environment.