Applications are accepted until further notice.  

Meet the Team

At Foundation AI, we are leading frontier AI research across Cisco. Our mission is to advance the state of AI. We translate that research into purpose-built models, agentic systems, new algorithms, optimization methods, and scalable data processing techniques that solve real-world problems at scale.

We explore new model architectures, pre-training and post-training methods, inference optimization, evaluation techniques, and data pipelines. Together, we are advancing AI at Cisco and for the broader AI community!

Our Infrastructure & Systems team builds the foundation that makes this research possible. We work closely with researchers to turn evolving experimental workflows into reliable, reusable systems for data preparation, model training, post-training, evaluation, and inference. Our work spans distributed GPU computing, cloud infrastructure, research tooling, and the systems that connect experiments, datasets, models, and results.

Your Impact

As an AI Researcher on the infrastructure team of Foundation AI, you will design, build, and operate the platforms that help our researchers move from an idea to a reproducible experiment and a deployable model. You will make complex AI workloads easier to launch, observe, debug, and scale.

You will own projects from identifying problems to building and operating solutions. This could be supporting a new training stack, improving GPU scheduling, building evaluation and rollout services, or making experiments easier to track and reproduce. Your work will help researchers test ideas faster, run experiments more reliably, and ensure we are using compute as efficiently as possible. You’ll also help enable automated research workflows, where agents build on previous results to run new experiments and conceive and test novel ideas.

We value engineers who understand researcher workflows, make thoughtful architectural tradeoffs, and turn evolving requirements into simple, reusable tools. You will look for signal to guide improvements in performance and reliability, debug problems across the software and infrastructure stack, and carry solutions through adoption and continued operation. You will also have opportunities to contribute to open-source projects, technical writing, and collaborative research.

In this role, you will:

• 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, including agent sandboxes, workload orchestration, scheduling, resource sharing, storage, and networking.
• 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, including progress tracking, failure recovery, resource cleanup, and durable outputs.
• Connect experiment configurations, code, datasets, checkpoints, and evaluation results so researchers and agents can reliably reproduce runs, compare outcomes, and build on prior work.
• Improve observability and performance across the stack, diagnosing bottlenecks in GPU utilization, data loading, checkpointing, distributed communication, and inference.
• Partner with research and data teams to build dependable data and evaluation workflows.
• Work directly with researchers to understand their workflows, debug failures, and deliver well-documented capabilities that teams adopt and use.

Minimum Qualifications

• Bachelors + 5 years of related experience, or Masters + 3 years of related experience, or PhD + 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 Qualifications

• Experience with distributed GPU training or post-training systems, including parallelism strategies, checkpointing, recovery, and performance profiling.
• Familiarity with frameworks such as Megatron, NeMo, VERL, or comparable distributed training and reinforcement learning systems.
• Experience deploying and optimizing LLM serving systems such as vLLM or SGLang, including throughput, latency, memory use, and concurrency.
• Experience building research platforms, experiment tracking, dataset or model catalogs, evaluation harnesses, or execution environments for AI agents.
• Experience with infrastructure as code, cloud identity and access management, secure workload isolation, and operational monitoring across platforms such as AWS, GCP, or specialized GPU clouds.
• A record of improving developer productivity, reliability, or compute efficiency through systems adopted by other engineers or researchers.
• Contributions to open-source infrastructure or ML systems, technical publications, or familiarity with cybersecurity and security-focused AI applications.

Why Cisco? 

At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint.

Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere. 

We are Cisco, and our power starts with you. 

Message to applicants applying to work in the U.S. and/or Canada:

Individual pay is determined by the candidate's hiring location, market conditions, job-related skillset, experience, qualifications, education, certifications, and/or training. The full salary range for certain locations is listed below. For locations not listed below, the recruiter can share more details about compensation for the role in your location during the hiring process.

U.S. employees are offered benefits, subject to Cisco’s plan eligibility rules, which include medical, dental and vision insurance, a 401(k) plan with a Cisco matching contribution, paid parental leave, short and long-term disability coverage, and basic life insurance. Please see the Cisco careers site to discover more benefits and perks.  Employees may be eligible to receive grants of Cisco restricted stock units, which vest following continued employment with Cisco for defined periods of time.

U.S. employees are eligible for paid time away as described below, subject to Cisco’s policies:

• 10 paid holidays per full calendar year, plus 1 floating holiday for non-exempt employees

• 1 paid day off for employee’s birthday, paid year-end holiday shutdown, and 4 paid days off for personal wellness determined by Cisco

• Non-exempt employees** receive 16 days of paid vacation time per full calendar year, accrued at rate of 4.92 hours per pay period for full-time employees

• Exempt employees participate in Cisco’s flexible vacation time off program, which has no defined limit on how much vacation time eligible employees may use (subject to availability and some business limitations)

• 80 hours of sick time off provided on hire date and each January 1st thereafter, and up to 80 hours of unused sick time carried forward from one calendar year to the next

• Additional paid time away may be requested to deal with critical or emergency issues for family members

• Optional 10 paid days per full calendar year to volunteer

For non-sales roles, employees are also eligible to earn annual bonuses subject to Cisco’s policies.

Employees on sales plans earn performance-based incentive pay on top of their base salary, which is split between quota and non-quota components, subject to the applicable Cisco plan. For quota-based incentive pay, Cisco typically pays as follows:

• .75% of incentive target for each 1% of revenue attainment up to 50% of quota;

• 1.5% of incentive target for each 1% of attainment between 50% and 75%;

• 1% of incentive target for each 1% of attainment between 75% and 100%; and

• Once performance exceeds 100% attainment, incentive rates are at or above 1% for each 1% of attainment with no cap on incentive compensation.

For non-quota-based sales performance elements such as strategic sales objectives, Cisco may pay 0% up to 125% of target. Cisco sales plans do not have a minimum threshold of performance for sales incentive compensation to be paid.

The applicable full salary ranges for this position, by specific state, are listed below:

New York City Metro Area:

Non-Metro New York state & Washington state:

* For quota-based sales roles on Cisco’s sales plan, the ranges provided in this posting include base pay and sales target incentive compensation combined.

** Employees in Illinois, whether exempt or non-exempt, will participate in a unique time off program to meet local requirements.