Research suggests the superior predictive capabilities of algorithms compared to humans. However, people's reluctance to use algorithms after witnessing their inaccuracies has hindered their widespread adoption. Studies have explored this reluctance, but little is known about how different people use algorithms. We focused on algorithm utilisation by engineers, conducting two longitudinal ecological momentary assessment studies outside the lab to explore differences in how engineers and non-engineers engage with inaccurate algorithms. These studies involved 427 participants, predicting currency exchange rates or maximum weather temperatures over nine days based on the judge-advisor system framework. Our results showed a significant three-way interaction between the effects of advice source, whether participants were engineers or non-engineers, and time. Specifically, the trend of inaccurate algorithm use significantly decreased over time for engineers, highlighting the importance of considering the end-users when implementing algorithms.