The performance of scheduling algorithms for HPC jobs highly depends on the accuracy of job runtime values. Prior research has established that neither user-provided runtimes nor system-generated runtime predictions are accurate. We propose a new scheduling platform that performs well in spite of runtime uncertainties. The key observation that we use for building our platform is the fact that two important classes of scheduling strategies (backfilling and plan based) differ in terms of sensitivity to runtime accuracy. We first confirm this observation by performing trace-based simulations to characterize the sensitivity of different scheduling strategies to job runtime accuracy. We then apply gradient boosting tree regression as a meta-learning approach to estimate the reliability of the system-generated job runtimes. The estimated prediction reliability of job runtimes is then used to choose a specific class of scheduling algorithm. Our hybrid scheduling platform uses a plan-based scheduling strategy for jobs with high expected runtime accuracy and backfills the remaining jobs on top of the planned jobs. While resource sharing is used to minimize fragmentation of resources, a specific ratio of CPU cores is reserved for backfilling of less predictable jobs to avoid starvation of these jobs. This ratio is adapted dynamically based on the resource requirement ratio of predictable jobs among recently submitted jobs. We perform extensive trace-driven simulations on real-world production traces to show that our hybrid scheduling platform outperforms both pure backfilling and pure plan-based scheduling algorithms.