Introduction:
This position as a Machine Learning Engineer on our team will involve working primarily on the Google Cloud Platform (Google Cloud Platform) for tasks such as inference, deployment automation, experimentation, and sampling. The role also includes integrating Java-based streaming pipelines, working with hybrid infrastructure systems, and utilizing machine learning frameworks like TensorFlow, PyTorch, and JAX.
Responsibilities:
- Evaluate and benchmark new ML inference frameworks
- Deploy models to Google Cloud Platform and integrate them into production applications
- Own deployment automation end-to-end
- Monitor model behavior in production for real end-users
- Design and execute benchmarking, performance testing, and quality testing on ML models
- Collaborate with ML researchers to provide benchmarking feedback and guide inference decisions
Requirements:
Required Skills:
- Machine Learning (ML) knowledge
- Experience with Cloud Computing and Google Cloud Platform (Google Cloud Platform)
- Proficiency in machine learning frameworks like PyTorch, JAX, TensorFlow
- Ability to work with Java-based streaming pipelines
- Experience deploying models in cloud environments, preferably Google Cloud Platform
Preferred Skills:
- Exposure to Java or JVM-based systems
- Familiarity with streaming data architectures
- Experience in hybrid cloud/on-prem environments
Education:
Bachelor''s or Master''s degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field.
Disclaimer:
GlobalLogic estimates the starting pay range for this role to be performed remotely to be $125,000 to $135,000 and reflects base salary only. This pay range is provided as a good-faith estimate, and the amount offered may be higher or lower. GlobalLogic takes many factors into consideration in making an offer, including candidate qualifications, work experience, operational needs, travel and onsite requirements, internal peer equity, prevailing wage, responsibilities, and other market and business considerations.
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