Senior Data Scientist - AI Model Optimization (LLM/SLM) for Network Automation

As the global leader in high-speed connectivity, Ciena is committed to a people-first approach. Our teams enjoy a culture focused on prioritizing a flexible work environment that empowers individual growth, well-being, and belonging. We’re a technology company that leads with our humanity—driving our business priorities alongside meaningful social, community, and societal impact.

Are you ready to revolutionize network troubleshooting and operations through cutting-edge AI?

Ciena is seeking a highly skilled Data Scientist to lead the development of domain-specific Small Language Models (SLMs) and other distilled AI models that enable real-time or near-real-time predictions, diagnostics, and resolutions for complex network issues. Your work will empower Ciena’s software platforms, like Blue Planet, to achieve unprecedented levels of automation and intelligence.
 

How You Will Contribute:
 

Model Development & Optimization:

• Design and implement knowledge distillation pipelines to compress large foundational models into smaller, faster, and highly efficient "student" models for deployment in resource-constrained environments.

• Fine-tune foundational language models (LLMs) using Ciena’s unique datasets, employing advanced techniques like instruction tuning, LoRA, and QLoRA.

• Develop specialized Small Language Models (SLMs) for network troubleshooting tasks, including:

  • Root Cause Analysis: Identifying the primary source of network faults.

  • Log Summarization: Condensing verbose syslog and event data into actionable summaries.

  • Next-Best-Action Recommendations: Suggesting corrective actions for network operators.

  • Anomaly Detection: Interpreting deviations in network telemetry to predict potential failures.
     

Research & Data-Centric AI:

• Stay at the forefront of AI/ML research, applying emerging techniques in model optimization, NLP, and time-series analysis to solve complex networking challenges.

• Collaborate with network subject matter experts and data engineers to curate high-quality training datasets, perform exploratory data analysis (EDA), and engineer features from network telemetry data.
   

MLOps & Deployment:

• Package and containerize models using tools like Docker and deploy them within Ciena’s automation platforms.

• Monitor model performance in production and establish pipelines for continuous retraining and improvement.
   

The Must Haves:

• Master’s degree or PhD in Computer Science, Electrical Engineering, Statistics, or a related quantitative field.

• 2+ years of hands-on experience as a Data Scientist or Machine Learning Engineer.

• Proven experience fine-tuning Large Language Models (LLMs) and adapting them to specific domains.

• Strong expertise in model compression techniques like knowledge distillation, quantization, or pruning.

• Expert-level proficiency in Python and core data science libraries (e.g., Pandas, NumPy, Scikit-learn).

• Deep experience with one major deep learning framework: PyTorch (preferred), TensorFlow, or JAX.

• Proficiency with NLP tools, particularly the Hugging Face stack (Transformers, PEFT, Datasets).
   

The Assets:

• Experience in the telecommunications or networking industry, including familiarity with network data sources like SNMP, syslog, gNMI/gRPC, and network alarms.

• Knowledge of multi-layer networking, from optical (DWDM, OTN) to packet (L2 switching, L3 routing/IP/MPLS).

• Experience deploying models on cloud platforms like GCP Vertex AI, AWS SageMaker, or Azure ML.

• Familiarity with MLOps tools and principles (e.g., MLflow, Kubeflow, CI/CD pipelines for models).

• Published research in top-tier AI/ML conferences or journals.

Pay Range:
 

Canada: $81,600.00 - $130,400.00

USA:$91,000 - $145,400.00

#LI-JD

Pay ranges at Ciena are designed to accommodate variations in knowledge, skills, experience, market conditions, and locations, reflecting our diverse products, industries, and lines of business. Please note that the pay range information provided in this posting pertains specifically to the primary location, which is the top location listed in case multiple locations are available.

Non-Sales employees may be eligible for a discretionary incentive bonus, while Sales employees may be eligible for a sales commission. In addition to competitive compensati