It’s an exciting moment when you finally secure a Machine Learning Job after rounds of interviews and a lengthy job search. You envision yourself diving into cutting-edge AI projects and applying your skills to solve complex problems. However, the reality can sometimes be quite different. For one Machine Learning Engineer, the dream job turned into something unexpected, highlighting a common challenge in the field.
Four months into a new role on the AI team at a large corporation, this engineer found themselves in a situation far removed from actual machine learning tasks. Initially assigned to a project that had stumped other team members, the project centered around Natural Language Processing (NLP). The immediate hurdle? A mere 200 unlabeled examples to work with. Compounding the problem, the data was described as significantly more intricate than typical datasets used in standard NLP models, bordering on challenges that might require artificial general intelligence to truly overcome.
Faced with these constraints, the engineer opted for a pragmatic approach, developing a 100% rules-based system. After considerable refinement, this system functioned reasonably well on the limited dataset provided. Efforts to obtain more data were met with weeks of internal maneuvering, ultimately yielding irrelevant data.
This experience raises questions about the nature of some “machine learning jobs”. While the title suggests advanced AI application, the daily reality can involve navigating data scarcity and management’s perhaps unrealistic expectations. There seems to be a perception that hiring “smart” individuals equates to instant AI solutions, even in the absence of sufficient data – the very fuel that machine learning models need to operate. This sentiment was echoed by newer team members who openly expressed their desire to engage in “real Machine Learning” projects, indicating a broader disconnect between job descriptions and actual tasks.
For a Machine Learning Engineer, the lack of data is a fundamental impediment. It’s akin to being a fish out of water, unable to utilize core skills and contribute effectively. While four months might seem premature to evaluate a new position, the engineer in this scenario is understandably concerned about the long-term implications. If this trajectory continues, it could hinder career growth and lead to skill stagnation. Having previously spent 1.5 years in a prior role before this one, the engineer is now contemplating whether seeking a new machine learning job might be necessary, even at this early stage. The question remains: is four months too soon to consider moving on when the promised machine learning reality is nowhere in sight?
