> Markdown version of [/jobs/ext/2074763-lead-machine-learning-engineering-hybrid](https://www.wearedevelopers.com/jobs/ext/2074763-lead-machine-learning-engineering-hybrid). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Machine Learning Engineering, (Hybrid) - **Company:** Cisco Systems, Inc. - **Location:** Seattle, WA, United States - **Experience:** Expert - **Salary:** $197,500.0 - $249,800.0 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, C++ (Programming Language), Information Engineering, Distributed Computing Environment, Python (Programming Language), Machine Learning, Tensorflow, Data Ingestion, Pytorch, Large Language Models, Apache Spark, Data Pipelines, Cisco, Data Generation - **Published:** August 15, 2026 - **Apply:** https://dejobs.org/x/x/5BBF0A93A9C54053AC720499B8774848/job/ ## About the Role * 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., * 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. ## Description This is a hands-on technical role at the intersection of machine 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. ## Related Videos - [Unlocking the Power of AI: Accessible Language Model Tuning for All](https://www.wearedevelopers.com/videos/951-unlocking-the-power-of-ai-accessible-language-model-tuning-for-all) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) - [Computer Vision from the Edge to the Cloud done easy](https://www.wearedevelopers.com/videos/263-computer-vision-from-the-edge-to-the-cloud-done-easy) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)