Lead Machine Learning Engineering, (Hybrid)

Cisco Systems, Inc.; Seattle (WA); Hybrid; USD 180,000 - 260,000; Full time

Job summary

Cisco Systems, Inc. in Seattle/San Jose is seeking a Lead Machine Learning Engineer to build and scale data pipelines powering our AI models. You will work across ML engineering and data engineering, shaping data needs and measuring impact on model performance.

You will design end-to-end data systems, manage human-in-the-loop labeling workflows, and implement automated quality controls to deliver high-fidelity training data for LLMs and AI systems.

Qualifications

• Design, build, and maintain scalable data pipelines for ML/LLM development.
• Architect and manage human-in-the-loop labeling workflows with quality controls.
• Collaborate with researchers and engineers to align data needs with model goals.

Responsibilities

• Design, build, and maintain robust, scalable data pipelines for ML/LLM development.
• Architect and manage human-in-the-loop labeling workflows with quality control.

Skills

Data pipelines

Human-in-the-loop labeling

ML systems

Job description

The application window is expected to close on: 09/28/2026

Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received.

This is a hybrid role based out of Cisco's Seattle or San Jose office.

Meet the Team

The Cisco AI Research team brings together AI researchers, machine learning engineers, data engineers, and networking domain experts to build the next generation of AI-powered networking.

We work at the intersection of generative AI, large-scale data systems, and networking, developing Large Language Models (LLMs), agents, and domain-specific AI systems. Our work spans research and engineering, with a strong focus on translating advances in AI into scalable systems and real-world impact.

Your Impact

As a Lead Machine Learning Engineer, you will build and improve the data and ML systems that power our LLMs and AI models.

A major focus of this role is solving one of the most important challenges in modern AI: creating high-quality training and evaluation data at scale. You will design and build scalable data pipelines, improve human data labeling workflows, create synthetic datasets, and develop automated approaches for continuously measuring and improving dataset quality.

This is a hands-on technical role at the intersection of machine learning engineering and data engineering. You will work closely with researchers, engineers, and domain experts to determine what data our models need, how to create it efficiently, and how to measure its impact on model performance.

• Design, build, and maintain robust, scalable data pipelines that support the full lifecycle of ML and LLM development, from initial data ingestion to production-ready model deployment.

• Architect and manage human-in-the-loop labeling workflows, including task generation, quality control, and feedback integration to ensure high-fidelity training data