diff --git a/content/en/events/upcoming-events/gsoc-2026.md b/content/en/events/upcoming-events/gsoc-2026.md
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@@ -104,3 +104,35 @@ To participate in GSoC with Kubeflow, you **must** meet the GSoC [eligibility re
 
 ---
 
+## Project 3: OptimizationJob CRD for Hyperparameter Optimization
+
+**Components:** [kubeflow/katib](https://www.github.com/kubeflow/katib), [kubeflow/sdk](https://www.github.com/kubeflow/sdk), [kubeflow/trainer](https://www.github.com/kubeflow/trainer)
+
+**Mentors:** [@akshaychitneni](https://github.com/akshaychitneni), [@andreyvelich](https://github.com/andreyvelich)
+
+**Contributor:**
+
+**Details:**
+
+Hyperparameter optimization (HPO) is critical for maximizing model performance in machine learning workflows. While Katib currently provides HPO capabilities through the `Experiment` CRD, it was designed for broad use cases including Neural Architecture Search (NAS) and arbitrary workloads.
+
+This project aims to design and implement a new **OptimizationJob CRD** (`optimizer.kubeflow.org/v1alpha1`) specifically focused on hyperparameter optimization for TrainJobs. The new CRD will provide:
+
+- **Tighter TrainJob Integration**: Replace unstructured trial specifications with typed TrainJob templates, enabling strong validation
+- **Shared Initialization**: Implement a common initializer pattern that runs once and shares model/dataset artifacts across all trials reducing trial startup time and storage costs
+- **Simplified API**: Focus exclusively on HPO use cases
+- **Modern Metrics Collection**: Support push-based metrics reporting via the Kubeflow SDK
+- **SDK Alignment**: Integrate with `OptimizerClient` API from [KEP-46: Hyperparameter Optimization in Kubeflow SDK](https://github.com/kubeflow/sdk/blob/main/docs/proposals/46-hyperparameter-optimization/README.md)
+
+Tracking issue: [kubeflow/katib#2605](https://github.com/kubeflow/katib/issues/2605)
+
+**Difficulty:** Hard
+
+**Size:** 350 hours (Large)
+
+**Skills Required/Preferred:**
+* Go
+* Python
+* Familiarity with Kubernetes controllers, CRDs
+* Basic understanding of machine learning training workflows
+* Experience with HPO frameworks
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