Lead ML Engineer: Scalable Data Pipelines & LLMs

Cisco Systems, Inc; Seattle (WA); Hybrid; USD 198,000 - 287,000; Full time

Benefits offered by this job

Medical insurance

401(k) with Cisco matching

Paid parental leave

Disability coverage

Job summary

Cisco Systems, Inc. is seeking a Lead Machine Learning Engineer in a hybrid role based out of Seattle, WA or San Jose, CA.

You will lead data and ML systems powering our LLMs, building scalable pipelines from data ingestion to production deployment, and shaping the quality and diversity of training datasets. You will work closely with researchers, engineers, and domain experts to determine data needs, optimize labeling workflows, and improve model performance through automated data generation

Qualifications

• 8+ years in STEM field with or without advanced degree
• Hands-on experience building and scaling ML data pipelines
• Proficiency with ML frameworks (PyTorch, TensorFlow) and production systems

Responsibilities

• Design, build, and maintain scalable data pipelines for ML/LLMs from ingestion to deployment
• Architect and manage human-in-the-loop labeling workflows with QA and feedback loops
• Develop synthetic data generation, filtering, and validation strategies
• Automate data generation, labeling, scoring, and evaluation processes for model performance
• Measure dataset quality and mitigate failure modes like bias and distribution shifts
• Collaborate with researchers and ML engineers to define dataset requirements for fine-tuning and agents
• Provide technical direction on infrastructure, compute, and storage; mentor team members

Skills

Python programming

Data engineering

Machine learning concepts

Collaboration

Education

Bachelor's in STEM

Master's in STEM

PhD in STEM

Tools

PyTorch

TensorFlow

Spark

Ray

Beam

Job description

Cisco Systems, Inc. is seeking a Lead Machine Learning Engineer in a hybrid role based out of Seattle, WA or San Jose, CA.

You will lead data and ML systems powering our LLMs, building scalable pipelines from data ingestion to production deployment, and shaping the quality and diversity of training datasets. You will work closely with researchers, engineers, and domain experts to determine data needs, optimize labeling workflows, and improve model performance through automated data generation