Principal Machine Learning Engineer

The application window is expected to close on: 12/23/2026

This is a hybrid role, requiring 3 days per week on site in San Jose, CAMeet The Team Cisco's CX AI Incubation and Agents team owns the foundational AI platform behind intelligent customer experiences across Cisco—the shared layer that application and agent teams across CX build on, in cloud, on-premises, and fully air-gapped customer environments.

Our charter spans the development of AI-native platforms – including model and inference layers, evaluation systems for shipped AI behavior, agentic and orchestration infrastructure, foundational AI services and APIs, and packaging and upgrade capabilities – as well as breakthrough solutions across delivery intelligence, network automation, and edge computing. Our work spans classical ML, domain-specific language models, and Agentic AI.Your Impact As a Principal Machine Learning Engineer on Cisco’s CX AI Incubation and Agents team, you tackle ambiguous, high-impact challenges and lead cross-functional engagements from architecture and design through production and customer deployment.

You will serve as the architectural and technical authority for designing, building, and scaling the AI foundation powering Cisco IQ and evolving customer needs. As a hands-on technical leader, you will design and build AI systems for production scale. You will set the benchmark for system design, platform governance, and technical excellence across the CX engineering ecosystem.

What You Will Own & Drive Foundational AI Platform Architecture:
Design and build robust, multi-tenant AI services with scalable APIs, versioning, backward compatibility, and quota management across cloud, on-premises, and air-gapped environments.

AI Technology Strategy and Execution:
Select, evaluate, and implement models, frameworks, inference runtimes, and orchestration technologies. Translate emerging capabilities into production architectures optimized for scalability, latency, memory, and compute across GPU, CPU, and edge environments.

AI Cost Optimization and Standards:
Design and implement cost optimization across workloads by improving token efficiency, model selection, inference performance, and compute utilization. Establish measurable engineering standards for cost efficiency.

Technical Governance & Leadership by Influence:
Lead architecture and design reviews, resolve complex technical trade-offs, mentor senior engineers, and influence platform direction through hands-on technical leadership and execution.

Minimum Qualifications:

Bachelors + 14 years of related experience, or Masters + 11 years of related experience, or PhD + 7 years of related experience.

Experience developing, fine-tuning, and optimizing models in Python, PyTorch or JAX, using techniques such as PEFT or LoRA, quantization, and distillation.

Experience architecting and deploying production LLM and/or SLM inference runtimes optimized 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 for AI systems, including golden test datasets, LLM-as-a-judge methodologies, drift detection, and/or CI/CD regression gates.

Experience architecting production AI platform APIs featuring robust quota management, versioning, and deployment across hybrid cloud and air-gapped environments.

Preferred Qualifications:

Research & Thought Leadership:
Recognized track record of industry contributions via publications, patents, conference presentations, or core contributions to open-source AI/ML projects.

Edge & Resource-Constrained Inference:
Hands-on experience optimizing inference workloads…