Description

Remote Work: Hybrid

Overview:

At Zebra, we are a community of innovators who come together to create new ways of working. United by curiosity and a culture of caring, we develop smart solutions that anticipate our customer’s and partner’s needs and solve their challenges.

Being a part of Zebra Nation means you are seen, heard, valued, and respected. Drawing from our unique perspectives, we collaborate to deliver on our purpose. Here you are a part of a team pushing boundaries today to redefine the work of tomorrow for organizations, their employees, and those they serve.

You'll have opportunities to learn and lead in a forward-thinking environment, defining your path to a fulfilling career while channeling your skills toward causes you care about - locally and globally.

Come make an impact every day at Zebra.

LLM model Engineer:

Responsible for developing, fine-tuning, and maintaining large language models (LLMs) that power the Workcloud AI Platform. Works closely with AI Agent Developers and Data Scientists to operationalize models and enable contextual intelligence across tasks.

Responsibilities:

• Strong expertise in large language models, including fine-tuning and post-training optimization techniques.
• Fine-tune open-source or proprietary LLMs using domain data.
• Design embeddings and prompt structures for multi-turn reasoning.
• Integrate models with APIs and inference pipelines.
• Collaborate with the Prompt Engineer for alignment and quality assurance.
• Measure and optimize model performance (latency, accuracy, and cost).
• LLM's Post-training related experience: Experience in utilizing Supervised Fine-Tuning and Parameter Efficient Fine-Tuning.

Skills Required:

• Strong in Python and model deployment frameworks (Hugging Face, LangChain).
• Understanding of transformer architectures, embeddings, and vector stores.
• Exposure to MLOps and inference optimization tools.

Qualifications:

• All other Regions:
  • Bachelor's Degree
  • 1 to 2 years of experience
  • Quantitative and problem solving skills
  • • Strong expertise in large language models, including fine-tuning and post-training optimization techniques.
    • Fine-tune open-source or proprietary LLMs using domain data.
    • Design embeddings and prompt structures for multi-turn reasoning.
    • Integrate models with APIs and inference pipelines.
    • Collaborate with the Prompt Engineer for alignment and quality assurance.
    • Measure and optimize model performance (latency, accuracy, and cost).
    • LLM's Post-training related experience: Experience in utilizing Supervised Fine-Tuning and Parameter Efficient Fine-Tuning.
    
    Skills Required:
    
    • Strong in Python and model deployment frameworks (Hugging Face, LangChain).
    • Understanding of transformer architectures, embeddings, and vector stores.
    • Exposure to MLOps and inference optimization tools.

To protect candidates from falling victim to online fraudulent activity involving fake job postings and employment offers, please be aware our recruiters will always connect with you via @zebra.com email accounts. Applications are only accepted through our applicant tracking system and only accept personal identifying information through that system. Our Talent Acquisition team will not ask for you to provide personal identifying information via e-mail or outside of the system. If you are a victim of identity theft contact your local police department.

Zebra Technologies leverages AI technology to evaluate job applications using objective, job-relevant criteria. This approach enhances efficiency and promotes fairness in the hiring process. However, every decision regarding interviews and hiring is made by our dedicated team, because we believe people make the best decisions about people. For more on how we use technology in hiring and how we process applicant data, see our Zebra Privacy Policy .