Machine Learning Engineer

Abaka AI

Palo Alto, CA, United States
Base: $175,000 - $275,000 usd annually; bonus/equi...
On-site
Multimodal model training
Scalable training pipelines
Distributed training systems
Abaka AI is seeking a Machine Learning Engineer to design and optimize scalable multimodal AI systems. The role involves collaboration with data engineering and research teams to enhance model training and performance, offering a chance to influence the technical direction of the company

Job Summary

  • You will own the design and development of scalable training pipelines, work directly with our data engineering and research teams, and help drive the technical roadmap for model development across multiple modalities.
  • As an early member of the engineering team, you will influence core decisions around model training strategy, experimentation frameworks, distributed infrastructure, and internal best practices.
  • The base salary range for this position is $175,000 - $275,000 USD annually.

Matching Summary

Match Score: 85

Abaka AI is seeking a Machine Learning Engineer to design and optimize scalable multimodal AI systems. The role involves collaboration with data engineering and research teams to enhance model training and performance, offering a chance to influence the technical direction of the company.

Salary

Base: $175,000 - $275,000 USD annually; Bonus/Equity: eligible for equity; Benefits: comprehensive benefits package (health, dental, vision, PTO, flexible work schedule)

Skills & Requirements

Must-have

  • multimodal model training
  • scalable training pipelines
  • distributed training systems
  • Python and ML frameworks
  • data workflows
  • model development roadmap

Nice-to-have

  • high-ownership environments
  • fast-moving AI company
  • elevate technical bar

Key Requirements

  • 3+ years of experience
  • Master’s degree or Ph.D. preferred
  • Proficient in Python
  • Experience with Kubernetes, Ray, Airflow, or MLflow
  • Cloud-based training environments

Work Rights

Not specified

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