Apple

Great team of AI/ML engineers.

Executive/Team leadership do not know the AI/ML real-world landscape and are deeply out of touch with the teams they 'lead'.

During my 5-month tenure as a Machine Learning (ML) engineer at Apple, I had the opportunity to work on the Siri team, focusing on bringing in massive data training to improve the virtual assistant's capabilities. While I was excited to contribute to a product that had the potential to revolutionize user interaction, I was often disheartened by the misguided decisions being made at the executive and team leader level. These decisions, in my opinion, hindered the team's progress and limited the potential of the project.

One of the most significant challenges I faced was the lack of clear direction and prioritization from leadership. Despite the team's best efforts, it seemed that decisions were often made without a deep understanding of the technical complexities and implications. This led to frustration and demotivation among team members, who felt that their expertise and input were not being valued. As a result, I found it difficult to make meaningful contributions to the project, as my ideas and suggestions were often dismissed or overlooked.

Working on the Siri team was a unique experience, as I was exposed to large-scale data processing and complex ML model training. However, I was often surprised by the lack of emphasis on technical excellence and the prioritization of features over substance. The team's focus on meeting deadlines and shipping products often took precedence over ensuring the quality and accuracy of the ML models. This approach, in my opinion, compromised the overall performance and reliability of Siri, and I worried about the long-term implications for users.

Throughout my time at Apple, I struggled to understand the decision-making process and the rationale behind certain choices. It seemed that short-term goals and metrics were prioritized over long-term vision and technical integrity.