Machine Learning Engineering Technical Leader at Cisco

Job description

The application window is expected to close on: 12/10/2026 Meet the Team Cisco’s Canvas AI team consists of AI researchers and software developers who collaborate to build innovative products and platforms for Cisco. We are motivated by tackling unique challenges that arise when connecting people and devices at a world-wide scale. Canvas is an agentic workspace where our customers’ IT teams and our AI agents investigate and resolve issues across every domain. It was built for teams that need to move from alert to evidence, from evidence to decision, and from decision to resolution faster. Your Impact As a MLE Tech Lead you will set the multi-quarter strategy for how Cisco measures agentic AI quality, owning evaluation from architecture through production operation. You will build layered, trustworthy evaluation systems (automated checks, calibrated LLM judges, and expert human review), extend them to live production traffic and simulation, and close the loop so failures turn into validated fixes. You will also drive shared measurement standards across partner teams and mentor engineers while representing the domain to senior leadership. Key responsibilities Own the evaluation roadmap and strategy for agentic AI quality, from architecture to production. Design a layered evaluation architecture: deterministic checks on every PR, model-based evaluation pre-release, and human expert calibration. Build and calibrate multi-model LLM-as-judge panels, tracking true-positive and true-negative rates separately. Run the human-in-the-loop flywheel with SMEs, turning annotations into training signal for automated judges. Score production traffic online, alert on regressions and drift, and convert real traces into offline test cases. Lead simulation, synthetic data, and adversarial testing without consuming lab resources. Close the loop from evaluation failure to clustered issue, candidate fix, and shadow/A/B validation. Ensure trustworthy results through experiment snapshots, dataset versioning, coverage reporting, and cost tracking. Drive common standards, data contracts, and judge criteria across partner teams without direct authority. Mentor engineers, set evaluation engineering standards, and represent the domain to senior leadership. Minimum Qualifications BA/BS with 8 years of relevant experience, MS with 6 years, or PhD with 3 years in Computer Science, Machine Learning or a related quantitative field. Experience taking machine learning or LLM-based systems into production and owning them operationally, not only building prototypes. Experience in Python and modern deep learning frameworks such as PyTorch or TensorFlow. Experience designing evaluation, measurement or quality systems for ML or LLM products — datasets, metrics, judges, benchmarks or regression suites. Experience setting technical direction across multiple teams through influence rather than reporting lines. Preferred Qualifications LLM-as-judge design and calibration: practical experience building judge systems and validating them against human experts, including inter-annotator agreement and judge drift. Agentic system evaluation: measuring multi-step reasoning, tool selection, trajectory quality and root-cause correctness rather than single-turn output quality. Production monitoring at scale: asynchronous scoring of live traffic, drift detection, and alerting that engineering teams actually act on. Simulation and synthetic environments: building reproducible test environments or synthetic data generation for domains where ground truth is scarce. Evaluation in CI/CD: integrating automated evaluation into build pipelines and using it to gate releases. Networking, observability or infrastructure domain knowledge: familiarity with the telemetry, topology and failure modes of enterprise network and observability systems. Cost-aware inference: routing, model selection and optimisation techniques that keep evaluation economically viable at production volume. Technical community contribution: publications at top ML venues, open-source contributions, or public technical writing in the evaluation space. 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 $216,300.00 to $280,800.00 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: $216,300.00 - $322,900.00 Non-Metro New York state & Washington state: $197,500.00 - $287,300.00 * 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.

Requirements

Must-haves

Key responsibilities Own the evaluation roadmap and strategy for agentic AI quality, from architecture to production.
Design a layered evaluation architecture: deterministic checks on every PR, model-based evaluation pre-release, and human expert calibration.
Build and calibrate multi-model LLM-as-judge panels, tracking true-positive and true-negative rates separately.
Run the human-in-the-loop flywheel with SMEs, turning annotations into training signal for automated judges.
Score production traffic online, alert on regressions and drift, and convert real traces into offline test cases.
Lead simulation, synthetic data, and adversarial testing without consuming lab resources.
Close the loop from evaluation failure to clustered issue, candidate fix, and shadow/A/B validation.
Ensure trustworthy results through experiment snapshots, dataset versioning, coverage reporting, and cost tracking.
Drive common standards, data contracts, and judge criteria across partner teams without direct authority.
Mentor engineers, set evaluation engineering standards, and represent the domain to senior leadership.
Minimum Qualifications BA/BS with 8 years of relevant experience, MS with 6 years, or PhD with 3 years in Computer Science, Machine Learning or a related quantitative field.
Experience taking machine learning or LLM-based systems into production and owning them operationally, not only building prototypes.
Experience in Python and modern deep learning frameworks such as PyTorch or TensorFlow.
Experience designing evaluation, measurement or quality systems for ML or LLM products — datasets, metrics, judges, benchmarks or regression suites.
Experience setting technical direction across multiple teams through influence rather than reporting lines.
Agentic system evaluation: measuring multi-step reasoning, tool selection, trajectory quality and root-cause correctness rather than single-turn output quality.
Production monitoring at scale: asynchronous scoring of live traffic, drift detection, and alerting that engineering teams actually act on.
Simulation and synthetic environments: building reproducible test environments or synthetic data generation for domains where ground truth is scarce.
Evaluation in CI/CD: integrating automated evaluation into build pipelines and using it to gate releases.
Networking, observability or infrastructure domain knowledge: familiarity with the telemetry, topology and failure modes of enterprise network and observability systems.
Cost-aware inference: routing, model selection and optimisation techniques that keep evaluation economically viable at production volume.
Technical community contribution: publications at top ML venues, open-source contributions, or public technical writing in the evaluation space.
We are Cisco, and our power starts with you.

Nice-to-haves

Preferred Qualifications LLM-as-judge design and calibration: practical experience building judge systems and validating them against human experts, including inter-annotator agreement and judge drift.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmmachine learningpythonmlci/cd

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Parental Leave

Wellness