Senior ML Engineer: Scalable Data & LLM Pipelines

Cisco Systems; Seattle (WA); Hybrid; USD 181,000 - 265,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) with Cisco match

Paid parental leave

Disability coverage

Life insurance

Job summary

Cisco AI Research is seeking a Senior Machine Learning Engineer in a hybrid role based in Seattle or San Jose. You will design end-to-end data pipelines and labeling workflows, build synthetic data, and apply ML techniques to automate data generation and evaluation.

Join a team blending AI research with scalable engineering to improve dataset quality, coverage, and model performance at scale.

Qualifications

• Strong programming in Python/C++/Go with production experience.
• Experience building and scaling ML data pipelines and datasets.
• Proven ability to design and deploy ML data systems end-to-end.
• Hands-on ML frameworks (PyTorch, TensorFlow) in research or production.

Responsibilities

• Design, build, and own end-to-end data pipelines for ML/LLM training and evaluation.
• Develop scalable labeling pipelines with quality control and feedback loops.
• Create synthetic data generation, augmentation, and validation workflows.
• Apply ML techniques to automate data generation, labeling, and evaluation.
• Measure dataset quality, coverage, bias, and distribution shifts.
• Build scalable infrastructure for processing large data volumes.
• Run experiments to link data quality to model performance improvements.
• Collaborate with researchers and engineers to produce datasets for training and evaluation.
• Translate research ideas into robust, production-ready data systems.
• Provide technical leadership via design reviews and best practices.

Skills

Python

C++

Go

PyTorch

TensorFlow

Education

Bachelor's degree in STEM

Master's degree in STEM

PhD in STEM

Tools

Spark

Ray

Beam

Job description

Cisco AI Research is seeking a Senior Machine Learning Engineer in a hybrid role based in Seattle or San Jose. You will design end-to-end data pipelines and labeling workflows, build synthetic data, and apply ML techniques to automate data generation and evaluation.

Join a team blending AI research with scalable engineering to improve dataset quality, coverage, and model performance at scale.