AI Researcher III – Infrastructure

Cisco

. Design, build, and operate platforms that help researchers move from ideas to reproducible experiments and deployable models .

Posted 11/5/2026full-timeSan Francisco • California • United StatesMid-LevelSenior💰 $184,000 - $238,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and operating AI platforms, focusing on reproducibility, model training, and deployment. Proficient in Python and PyTorch, with a strong understanding of scalable AI system design and data workflows.

Highest-signal resume keywords

Python ProgrammingPyTorch FrameworkModel Training and Fine-TuningData Pipeline DevelopmentDistributed GPU Training

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Artificial IntelligenceMachine LearningProduction-Grade Software EngineeringModel EvaluationOptimization TechniquesAutomated Container BuildsDependency ManagementPerformance ProfilingExperiment TrackingInfrastructure as Code

Soft Skills

Strong Communication SkillsCross-Functional Collaboration

Tools & Technologies

AWSGCPMegatronNeMoVERLVLLMSGLang

Industry Keywords

Reproducible ExperimentsAI-Agent Execution EnvironmentsSecure Workload IsolationOperational MonitoringCybersecurity

Tech Stack

Tools & technologies

AWSCloudCyber SecurityGoogle Cloud PlatformPythonPyTorch

About the role

Key responsibilities & impact

Design, build, and operate platforms that help researchers move from ideas to reproducible experiments and deployable models
Build shared platforms, services, and developer tools for pre-training, fine-tuning, reinforcement learning, evaluation, and inference
Develop and operate GPU and CPU computing infrastructure across clouds and regions
Build agent sandboxes, workload orchestration, scheduling, resource sharing, storage, and networking systems
Create reproducible training and serving environments through automated container builds, dependency management, compatibility validation, and release workflows
Build reliable rollout collection and isolated execution environments for agentic workloads
Connect experiment configurations, code, datasets, checkpoints, and evaluation results for reproducibility and comparison
Improve observability and performance across the stack
Diagnose bottlenecks in GPU utilization, data loading, checkpointing, distributed communication, and inference
Partner with research and data teams on dependable data and evaluation workflows
Work directly with researchers to understand workflows, debug failures, and deliver documented capabilities
Own projects from problem identification through solution adoption and continued operation

Requirements

What you’ll need

Bachelor's degree plus 5 years of related experience, or Master's degree plus 3 years of related experience, or PhD plus 0 years of related experience in Computer Science, Artificial Intelligence, Machine Learning, or a related field
Strong experience with Python, PyTorch, production-grade software engineering practices, and common AI/ML libraries
Experience with large language models, model training, fine-tuning, evaluation, inference, and optimization
Strong understanding of data pipelines, model experimentation, and scalable AI system design
Strong communication skills, with the ability to explain complex research and engineering concepts to cross-functional partners
Preferred: experience with distributed GPU training or post-training systems, including parallelism strategies, checkpointing, recovery, and performance profiling
Preferred: familiarity with Megatron, NeMo, VERL, or comparable distributed training and reinforcement learning systems
Preferred: experience deploying and optimizing LLM serving systems such as vLLM or SGLang
Preferred: experience building research platforms, experiment tracking, dataset or model catalogs, evaluation harnesses, or AI-agent execution environments
Preferred: experience with infrastructure as code, cloud identity and access management, secure workload isolation, and operational monitoring across AWS, GCP, or specialized GPU clouds
Preferred: record of improving developer productivity, reliability, or compute efficiency through systems adopted by engineers or researchers
Preferred: contributions to open-source infrastructure or ML systems, technical publications, or familiarity with cybersecurity and security-focused AI applications

Benefits

Comp & perks

Medical, dental and vision insurance
401(k) plan with a Cisco matching contribution
Paid parental leave
Short- and long-term disability coverage
Basic life insurance
Cisco restricted stock unit grants may be available
10 paid holidays per full calendar year
1 floating holiday for non-exempt employees
1 paid birthday day off
Paid year-end holiday shutdown
4 paid personal wellness days
16 days of paid vacation per full calendar year for non-exempt employees
Flexible vacation time off program for exempt employees
80 hours of sick time off provided on hire date and each January 1st, with up to 80 unused hours carried forward
Additional paid time away for critical or emergency family issues
Optional 10 paid volunteer days per full calendar year
Annual bonuses for non-sales roles, subject to Cisco policies
Opportunities to contribute to open-source projects, technical writing, and collaborative research