# Senior Machine Learning Engineer, AISWP (Hybrid) at Cisco

- Company: Cisco
- What the company does: Cisco is a worldwide technology leader powering an inclusive future for all. Learn more about our products, services, solutions, and innovations. Backed by Sequoia and Menlo.
- Company website: https://www.cisco.com/
- Type: Startups (AI role)
- Level: Senior
- Location: 2 Locations
- Work setup: On-site
- Posted: 2026-09-09
- Apply by: 2026-10-24
- Apply: https://cisco.wd5.myworkdayjobs.com/en-US/Cisco_Careers/job/San-Jose-California-US/Senior-Machine-Learning-Engineer--AISWP--Hybrid-_2023544
- Page: https://www.1752.vc/careers/jobs/cisco-senior-machine-learning-engineer-aiswp-hybrid/

## About the role

As a Senior 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.

## What they're looking for

- Bachelor’s degree in a STEM field with 7+ years of relevant experience, OR Master’s degree in a STEM field with 4+ years of relevant experience, OR PhD in STEM or a relevant technical field with 1+ years of industry or academic research experience
- 2+ years of hands-on experience building, curating, and scaling datasets for machine learning training and evaluation
- 3+ years of professional programming experience using Python, C++, or Go within a production or research environment
- 3+ years of experience using machine learning frameworks such as PyTorch, TensorFlow, or equivalent technologies to develop, train, evaluate, and deploy machine learning models
- Expertise in curating, scaling, and managing datasets for the entire LLM lifecycle—including synthetic data generation, augmentation, and post-training workflows like SFT and RLHF
- Proficiency in designing human-in-the-loop labeling systems and proactively mitigating complex dataset failure modes such as label noise, bias, contamination, and distribution shift