Ifm Us

Ifm Us

Distributed Machine Learning Engineer

Sunnyvale, CA

H1B sponsorship available$150k-$450kDetected 491 days ago
AlgorithmsMachine LearningDeep LearningResearchProblem Solving

About the role

  • The ideal candidate will have a strong background in parallel computing, and hands-on experience in system level coding, debug methodologies, and large-scale machine learning experience.

Responsibilities

  • Design and implement performance benchmarks and testing methodologies to evaluate application performance
  • Build tools to automate workload analysis, workload optimization, and other critical workflows
  • Support the team to develop appropriate kernels and systems for new model architectures and algorithms
  • Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
  • Perform all other duties as reasonably directed by the line manager that are commensurate with these functional objectives.

Requirements

  • Masters in CS, EE or CSEE or equivalent experience with 2+ year working experience

Compensation

  • $150k-$450k

Benefits

  • Include *Comprehensive medical, dental, and vision benefits *Bonus *401K Plan *Generous paid time off, sick leave and holidays *Paid Parental Leave *Employee Assistance Program *Life insurance and disability
  • Understand, analyze, profile, optimize, and provide guidance to the team on deep learning workloads on state-of-the-art hardware and software platforms to improve their efficiency with different levels of optimization
  • Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
  • Represent MBZUAI at industry conferences and events, showcasing the institution's cutting-edge HPC and deep learning capabilities and establishing MBZUAI as a global leader in AI research and innovation.

Visa & Work Authorization

  • This position is eligible for visa sponsorship.

This listing is sourced directly from Ifm Us's careers page and normalized into a canonical job model.