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Cisco
Principal Machine Learning Engineer
Cisco
Brooklyn•Hybrid•Salary•7-10 Yrs•1 hour ago
Job description
Hybrid
Overview
The application window is expected to close on: Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received.
Responsibilities
- Set and Drive Vision: Define and champion the strategic vision for AI and foundation models across Splunk and Cisco platforms, shaping the research and technology roadmap to anticipate and address industry-defining challenges.
- Architect and Lead Breakthroughs: Lead the end-to-end lifecycle of research, design, and deployment for large-scale foundation models targeting machine-generated data, with deep focus on logs and complementary modalities (time series, traces, events).
- Influence at Scale: Partner with executive leadership, engineering, product, and data science teams to ensure AI solutions align with broader organizational objectives, product strategies, and customer needs.
- Mentorship and Thought Leadership: Cultivate organizational excellence by mentoring senior technical talent, fostering research communities, and driving best practices in AI across global teams.
- Foster Innovation: Embed cutting-edge research and technological advances into products, driving sustained competitive advantage and transformation at enterprise scale.
Qualifications
- PhD in Computer Science, or related quantitative field, plus 7+ years of industry research experience.
- Proven track record in at least one of the following areas: large language modeling for both structure and unstructured data, deep learning-based time series modeling, advanced anomaly detection, and multi-modality modeling.
- Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
Nice to have
- Deep NLP & Domain-Adapted LLMs: Background in building and adapting large-scale language models (e.g., T5, BERT, LLaMA, GPTs) for specialized domains including structured/unstructured logs, text, and event sequences.
- Log Analytics Expertise - In-depth knowledge of structured/unstructured system logs, event sequence analysis, anomaly detection, and root cause identification.
- Advanced Anomaly Detection - Experience creating robust, scalable approaches (statistical, deep learning, or hybrid) for high-volume, real-time logs data.
- Multi-Modal AI Modeling - Strong track record fusing logs, time series, traces, tabular data, and graphs for foundation models tackling complex operational insights.
- Large-Scale Training & Optimization - Experience optimizing model architectures, distributed training pipelines, and inference efficiency to minimize cost and latency while preserving accuracy.
- MLOps & Continuous Learning - Fluency in automated retraining, drift detection, incremental updates, and production monitoring of ML models.
- Strong Research Track Record - Publications in top AI/ML conferences or journals (e.g., NeurIPS, ICML, ICLR, AAAI, CVPR, ACL, KDD) demonstrating contributions to state-of-the-art methods and real-world applications.