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XML and the XPath querying language have become ubiquitous data and querying standards used in many industrial settings and across the World-Wide Web. The high latency of XPath queries over large XML databases remains a problem for many applications. While this latency could be reduced by parallel execution, issues such as work partitioning, memory contention, and load imbalance may diminish the benefits of parallelization. We propose three parallel XPath query engines: Static Work Partitioning, Work Queue, and ProducerConsumer-Hybrid. All three engines attempt to solve the issue of load imbalance while minimizing sequential execution time and overhead. We analyze their performance on sets of synthetic and real-world datasets. Results obtained on two multi-core platforms show that while load-balancing is easily achieved for most synthetic datasets, real-world datasets prove more challenging. Nevertheless, our Producer-Consumer-Hybrid query engine achieves good results across the board (speedup up to 6.31 on an 8-core platform).