below marketmost similar roles pay hereabove market
This role pays more than
96%
of similar roles. Most pay
$198,000–$254,750
— the blue band above.
At the midpoint, this role pays about
$336k
versus about
$226k
for comparable roles.
Based on 240 similar postings.
Employer
About Cisco
Cisco Systems is the world''s leading networking technology company, designing and manufacturing networking hardware, telecommunications equipment, and cybersecurity solutions for businesses and governments. Industry: Networking Technology & Cybersecurity
Cisco currently has
307 open roles
on FindRole.
Listed pay typically runs
$167,700–$245,000
across 224 roles with salary data.
The Principal Machine Learning Engineer joins the CX AI Incubation and Agents team to develop the foundational AI platform for intelligent customer experiences. This role involves designing and building multi-tenant AI services, scalable APIs, and inference runtimes across cloud, on-premises, and air-gapped environments. You will lead the architecture for Cisco IQ, focusing on model selection, cost optimization, and platform governance. Key responsibilities include developing agentic workflows, RAG systems, and automated evaluation frameworks. Required technical skills include Python, PyTorch, and JAX, with expertise in PEFT, LoRA, quantization, and distillation. You must also possess experience with continuous batching, KV-cache management, and vector databases. The work addresses complex problems in delivery intelligence, network automation, and edge computing using classical ML, domain-specific language models, and Agentic AI.
What does a Machine Learning Engineer earn in California?
Median $235750 from 200 postings across 34 companies.
Design and build multi-tenant AI services with scalable APIs for cloud, on-premises, and air-gapped environments.
Select and implement models, frameworks, inference runtimes, and orchestration technologies for production use.
Optimize AI costs by improving token efficiency, model selection, and compute utilization across GPU and CPU environments.
Lead architecture and design reviews to resolve complex technical trade-offs and influence platform direction.
Develop and deploy production LLM/SLM inference runtimes using techniques like continuous batching and KV-cache management.
Architect production RAG systems and agentic workflows using vector databases.
Implement automated evaluation frameworks including golden test datasets, LLM-as-a-judge methodologies, and drift detection.
Mentor senior engineers and establish engineering standards for system design and platform governance.
What we're looking for
Bachelor's degree plus 14 years of related experience, Master's plus 11 years, or PhD plus 7 years of related experience.
Experience developing, fine-tuning, and optimizing models in Python, PyTorch, or JAX using PEFT, LoRA, quantization, and distillation.
Experience architecting and deploying production LLM and/or SLM inference runtimes with continuous batching, KV-cache management, or speculative decoding.
Experience architecting and deploying production RAG systems and agentic workflows using vector databases.
Experience designing and implementing automated evaluation frameworks including golden test datasets, LLM-as-a-judge, drift detection, and CI/CD regression gates.
Experience architecting production AI platform APIs with quota management, versioning, and deployment across hybrid cloud and air-gapped environments.
Recognized track record of industry contributions via publications, patents, conference presentations, or open-source AI/ML projects (preferred).
Hands-on experience optimizing inference workloads for resource-constrained compute environments like CPU, NPU, or small GPU footprints (preferred).