Lead Machine Learning Engineering, (Hybrid)

About this role

The application window is expected to close on: 09/28/2026 Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received .

This is a hybrid role based out of Cisco's Seattle or San Jose office.

Meet the Team

The Cisco AI Research team brings together AI researchers, machine learning engineers, data engineers, and networking domain experts to build the next generation of AI-powered networking.

We work at the intersection of generative AI, large-scale data systems, and networking, developing Large Language Models (LLMs), agents, and domain-specific AI systems. Our work spans research and engineering, with a strong focus on translating advances in AI into scalable systems and real-world impact.

Your Impact

As a Lead Machine Learning Engineer, you will build and improve the data and ML systems that power our LLMs and AI models .

A major focus of this role is solving one of the most important challenges in modern AI: creating high-quality training and evaluation data at scale . You will design and build scalable data pipelines, improve human data labeling workflows, create synthetic datasets, and develop automated approaches for continuously measuring and improving dataset quality.

This is a hands-on technical role at the intersection of m achine learning engineering and data engineering . You will work closely with researchers, engineers, and domain experts to determine what data our models need, how to create it efficiently, and how to measure its impact on model performance.

• Design, build, and maintain robust, scalable data pipelines that support the full lifecycle of ML and LLM development, from initial data ingestion to production-ready model deployment.

• Architect and manage human-in-the-loop labeling workflows, including task generation, quality control, and feedback integration to ensure high-fidelity training data.

• Develop scalable strategies for synthetic data generation, filtering, and validation to enhance dataset diversity, coverage, and overall quality.

• Leverage LLMs and advanced ML techniques to automate data generation, labeling, scoring, and evaluation processes, increasing efficiency and consistency.

• Establish rigorous systems to measure and mitigate dataset failure modes—such as bias, contamination, and distribution shifts—while designing experiments that directly link dataset composition to model performance.

• Collaborate closely with researchers and ML engineers to define dataset requirements for fine-tuning, preference learning, and agent development, ensuring alignment with project goals.

• Provide technical direction on infrastructure, compute, and storage decisions while fostering engineering excellence through design reviews, best practices, and team mentorship.

Minimum Qualifications • Bachelor’s degree in a STEM field with 8+ years of relevant experience, OR Master’s degree in a STEM field with 6+ years of relevant experience, OR PhD in STEM or a relevant technical field with 3+ years of industry or academic research experience.

• 3+ years of hands-on experience building, curating, and scaling datasets for machine learning training and evaluation.

• 5+ years of professional programming experience using Python, C++, or Go within a production or research environment.

• 5+ years of experience using machine learning frameworks such as PyTorch, TensorFlow, or equivalent technologies to develop, train, evaluate, and deploy machine learning models.

Preferred Qualifications

• Expertise in curating, scaling, and managing datasets for the entire LLM lifecycle—including synthetic data generation, augmentation, and post-training workflows like SFT and RLHF.

• Proficiency in designing human-in-the-loop labeling systems and proactively mitigating complex dataset failure modes such as label noise, bias, contamination, and distribution shift.

• Demonstrated success using LLMs for data generation, model-assisted labeling, and evaluation, with a focus on connecting iterative dataset changes to measurable improvements in model performance.

• Strong technical foundation in distributed data processing frameworks (e.g., Spark, Ray, Beam) and the ability to architect and deploy complex data engineering projects into production.

• A research-engineering mindset that bridges the gap between experimentation and production, combined with the communication skills to influence researchers, engineers, and product stakeholders.

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 $197,500.00 to $249,800.00 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 you

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