Senior ML Engineer - Data Pipelines for LLMs

020 Cisco Systems, Inc.; San Jose (CA); Hybrid; USD 203,000 - 259,000; Full time

Job summary

Cisco Systems, Inc. is seeking a Senior Machine Learning Engineer in a hybrid role based out of Cisco’s Seattle or San Jose office. You will design end‑to‑end data pipelines for ML/LLM training, improve labeling workflows, generate synthetic data, and automate data generation and evaluation.

The team focuses on scalable, production‑ready data systems with researchers and engineers. You will lead data quality efforts, measure bias and distribution shifts, and drive impactful improvements in model

Qualifications

• Bachelor’s degree in a STEM field with 7+ years of relevant experience or equivalent PhD/masters.
• 2+ years building, curating, and scaling ML datasets for training/evaluation.
• 3+ years programming in Python, C++, or Go in production or research.
• 3+ years using ML frameworks (PyTorch, TensorFlow) to train/evaluate models.

Responsibilities

• Design, build, and own end-to-end data pipelines for ML/LLM training and evaluation.
• Develop human-in-the-loop labeling, data collection, and annotation workflows.
• Create synthetic data generation, augmentation, and validation techniques.
• Apply LLMs to automate data generation, labeling, scoring, and evaluation.
• Measure dataset quality, bias, coverage, and distribution shifts.
• Build scalable infrastructure for processing large volumes of data.
• Run experiments to link dataset quality to model performance improvements.
• Collaborate with researchers/engineers to prepare datasets for training and evaluation.
• Translate research concepts into robust production data/ML systems.

Skills

Python

C++

Go

PyTorch

TensorFlow

Dataset curation

Education

Bachelor's degree in STEM

Master's degree in STEM

PhD in STEM

Tools

Spark

Ray

Beam

Job description

Cisco Systems, Inc. is seeking a Senior Machine Learning Engineer in a hybrid role based out of Cisco’s Seattle or San Jose office. You will design end‑to‑end data pipelines for ML/LLM training, improve labeling workflows, generate synthetic data, and automate data generation and evaluation.

The team focuses on scalable, production‑ready data systems with researchers and engineers. You will lead data quality efforts, measure bias and distribution shifts, and drive impactful improvements in model