Level

Cisco

Principal Machine Learning Engineer

Cisco is hiring a Principal Machine Learning Engineer in San Jose, United States. It pays $296k-$375k a year and Level rates it ; you can apply on Level.

AI in this role

pytorchjax
ragai-agentsfine-tuning
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, CA


Meet 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 for resource-constrained compute environments (CPU, NPU, small GPU footprints, and on-prem hardware appliances).

 

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:

The starting salary range posted for this position is $296,300.00 to $374,800.00 and reflects the projected salary range for new hires in this position in U.S. and/or Canada locations, not including incentive compensation*, equity, or benefits.

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:

$296,300.00 - $431,000.00

Non-Metro New York state & Washington state:

$263,700.00 - $383,500.00

* 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.

How we rate this

Principal Machine Learning Engineer at Cisco rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGAI agentsFine-tuningPyTorchJax

Also common in machine learning engineer jobs at this level

Pay

This job starts at
$296k
Typical machine learning engineer job
$200k

This posting starts 48% above the typical machine learning engineer job. See machine learning engineer jobs

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  4. What's a project where you used PyTorch hands-on?
  5. Walk me through how you've used Jax in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: RAG, AI agents, Fine-tuning, PyTorch, and Jax. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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