Lead Machine Learning Engineering, (Hybrid)
About The Position
The Cisco AI Research team is building the next generation of AI-powered networking by combining AI researchers, machine learning engineers, data engineers, and networking domain experts. They focus on generative AI, large-scale data systems, and networking, developing Large Language Models (LLMs), agents, and domain-specific AI systems. This role involves both research and engineering, with an emphasis on translating AI advancements into scalable systems with real-world impact. As a Lead Machine Learning Engineer, you will be responsible for building and enhancing the data and ML systems that support LLMs and AI models. A key aspect of this position is addressing the challenge of creating high-quality training and evaluation data at scale. This includes designing and implementing scalable data pipelines, improving human data labeling workflows, generating synthetic datasets, and developing automated methods for continuous quality assessment and improvement. This is a hands-on technical position at the intersection of machine learning engineering and data engineering. You will collaborate with researchers, engineers, and domain experts to define data needs, optimize creation processes, and measure data impact on model performance.
Requirements
- 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.
Nice To Haves
- 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.
Responsibilities
- 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.
Benefits
- medical, dental and vision insurance
- a 401(k) plan with a Cisco matching contribution
- paid parental leave
- short and long-term disability coverage
- basic life insurance
- Cisco restricted stock units
- 10 paid holidays per full calendar year
- 1 floating holiday for non-exempt employees
- 1 paid day off for employee’s birthday
- paid year-end holiday shutdown
- 4 paid days off for personal wellness
- 16 days of paid vacation time per full calendar year (non-exempt employees)
- flexible vacation time off program (exempt employees)
- 80 hours of sick time off provided on hire date and each January 1st thereafter
- up to 80 hours of unused sick time carried forward
- 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
- annual bonuses (for non-sales roles)
- performance-based incentive pay (for sales roles)
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What This Job Offers
Job Type
Full-time
Career Level
Senior
