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Takeaway.com

Senior Machine Learning Engineer

Applied Machine LearningUpdated on Nov 15
Full-timeNo salary listed

About the Job

Requirements

  • Proficiency in Python and a strong understanding of software engineering principles, including object-oriented design, design patterns, and testing, applied to machine learning.
  • Experience designing and operating machine learning systems in production, including familiarity with REST APIs, batch inference, and event-driven serving patterns.
  • Experience with orchestration tools such as Airflow or Dagster and cloud-native machine learning platforms such as AWS SageMaker or GCP Vertex AI.
  • Ability to influence infrastructure and tooling decisions and balance build-versus-buy considerations.
  • Strong knowledge of Infrastructure as Code using Terraform and containerization with Docker and Kubernetes.
  • Familiarity with data engineering fundamentals, including SQL and distributed data processing, to debug and optimize data flows.
  • Ability to automate manual processes and improve the developer experience for Data Scientists.

Responsibilities

  • Collaborate with Data Scientists, Product Managers, and Backend Engineers to connect model development with production systems.
  • Lead architectural design of end-to-end machine learning systems, including data ingestion, training pipelines, real-time inference, and monitoring infrastructure.
  • Transform data science prototypes into robust, scalable, and secure production software, owning the path to production.
  • Drive adoption of MLOps best practices, including CI/CD for machine learning, model versioning, and feature stores, to accelerate feedback for Data Scientists.
  • Communicate machine learning system trade-offs, such as latency versus accuracy, to technical and non-technical stakeholders.
  • Build and maintain relationships across Data and Engineering organizations to integrate machine learning systems with the wider platform.
  • Lead projects, mentor peers, and advocate for engineering excellence within the data science domain.
  • Drive architecture, write scalable and testable code, mentor engineers, and take responsibility for work from discovery through production to maintain system reliability and stability.

About the Company

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