Resumo da vaga

ML Infrastructure Engineer- (Early Career/Internship)

Requisitos e responsabilidades

Conteúdo da vaga extraído em seções para revisão mais rápida.

What you'll be doing

  • Build and maintain data pipelines that generate training datasets for machine learning models and experimentation
  • Contribute to infrastructure that supports distributed training workflows (e.g., PyTorch, Ray)
  • Work with workflow orchestration tools (e.g., Airflow, Flyte, or similar) to support multi-stage ML pipelines
  • Improve reproducibility and reliability through dataset validation, monitoring, and testing
  • Partner with ML engineers to support experimentation and model iteration
  • Help optimize performance and efficiency across data processing and training systems
  • Contribute to the evolution of our offline ML platform architecture as it scales

What we're looking for

  • Bachelor’s degree in Computer Science, Machine Learning, Systems, or a related field
  • Strong foundation in machine learning systems, distributed systems, or large-scale data processing (through research or projects)
  • Experience with Python and working with data-intensive workloads
  • Familiarity with ML frameworks (e.g., PyTorch, TensorFlow) and/or distributed systems (e.g., Ray, Spark)
  • Experience (academic or applied) with data pipelines, model training workflows, or large datasets
  • Strong problem-solving skills and ability to translate research ideas into practical systems
  • Interest in building scalable, reliable infrastructure for machine learning
  • Nice to Have
  • Experience with workflow orchestration systems (Airflow, Flyte, etc.)
  • Exposure to large-scale data platforms (data lakes, warehouses, streaming systems)
  • Publications or research in ML systems, distributed systems, or related areas

Additional information

  • Relocation support is not available for this position
  • Work visa/immigration sponsorship is not available for this position
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FocoMachine Learning EngineeringÁrea da vaga
Sinal de senioridadeJuniorNível do candidato
StackPython, SparkSkills principais
Localização1 país aceitoElegibilidade

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