top of page
Search

Why Do NLP Engineers Teach Machines Language?

Whenever an individual in Jakarta inputs a query into a search engine, inquires about their account balance via a banking application using voice commands, or interacts with a customer service chatbot on an e-commerce platform at 2 AM, a subtle yet significant engineering process is at work.

A device interprets human language, comprehending its significance, and responds in a manner that appears instinctive. All of that occurs intentionally. At the rear is an expert whose sole responsibility is to bridge the divide between human communication and computer processing: the natural language processing engineer.

The expression "instructing a machine in language" appears nearly lyrical, yet the endeavor is tangible, technical, and progressively significant. It is also located at the heart of one of the most rapidly expanding sectors of the technology economy.

Comprehending the motivations behind these engineers' actions elucidates much about the trajectory of software, commerce, and education, both worldwide and within Indonesia.


Why do NLP engineers teach machines language?

NLP engineers instruct machines in language since computers do not comprehend words as humans do. A processor operates with numerical values, not semantics.

To create a search engine, translation software, voice assistant, or functional chatbot, an engineer must transform disordered human language into a mathematical representation that the machine can comprehend and subsequently translate the machine's output back into readable text for humans.

The process of two-way translation, reiterated through millions of instances, encapsulates the essence of instructing a machine in language.

Engineers allocate resources to it for a straightforward reason: the majority of human-generated information consists of unstructured text and speech, and entities capable of interpreting, categorizing, and reacting to this deluge of language acquire a significant competitive edge.


What natural language processing really does

Natural language processing is a subdivision of artificial intelligence that enables computers to comprehend, analyze, produce, and react to human language.

The fundamental issue lies in representation. Lexical items possess significance for us, yet a model requires numerical data. Initially, an engineer divides text into smaller components known as tokens, subsequently converting these tokens into numerical vectors, frequently referred to as embeddings, which position analogous words in proximity within a mathematical framework. Within that context, the model can start to comprehend that "clinic" and "hospital" are interconnected or that "murah" and "terjangkau" both signify the concept of affordability.

For many years, word embedding techniques like word2vec accomplished this task by generating a singular vector for each word. The domain underwent a significant transformation in 2017 when Google researchers introduced the transformer architecture in their paper "Attention Is All You Need" (Vaswani et al., 2017).

Rather than reading a sentence exclusively from left to right, the transformer employs a mechanism known as "self-attention" to evaluate the interrelations of all words simultaneously. That singular concept rendered models significantly more parallel, considerably quicker to train, and markedly superior at maintaining long-range context.

It serves as the underlying framework for BERT (Devlin et al., 2018) and the extensive language models that currently generate, condense, and translate content on a large scale. Nearly every contemporary NLP system that engineers build today traces its roots back to that architecture.


The business reason behind the work

Organizations do not finance this endeavor for its esthetic appeal. They finance it due to the substantial and swiftly growing market for language technology.

The worldwide natural language processing market was estimated at USD 59.70 billion in 2024 and is anticipated to attain around USD 439.85 billion by 2030, expanding at a compound annual growth rate of nearly 38.7 percent (Grand View Research, 2025).

Various analysts report differing totals based on their delineation of the category, yet the trend remains uniform among firms: robust double-digit expansion for the remainder of the decade (Fortune Business Insights, 2025; MarketsandMarkets, 2026).

The primary factor is the substantial amount of unstructured language present within every organization. Customer correspondence, product evaluations, medical documentation, legal agreements, chat records, social media posts, and call transcripts all possess significance that a spreadsheet cannot encapsulate.

An NLP engineer builds systems that turn unrefined language into actionable insights for businesses, such as sentiment metrics that show customer emotions, classifiers that route support inquiries, summarizers that shorten long texts, and virtual assistants that answer questions on their own. Every one of those instruments eliminates expenses or generates understanding, which accounts for the persistent increase in demand.


Why the role pays well and keeps growing

Due to the challenging nature of the work and elevated demand, NLP engineers rank among the highest compensated professionals in software.

Data from prominent career platforms indicate that the average salary for an NLP engineer ranges from USD 122,000 to USD 150,000 annually, with senior professionals earning significantly higher amounts (Coursera, 2025a; Glassdoor, 2026).

The overarching trend is equally revealing. The U.S. Bureau of Labor Statistics indicates that computer and information research scientists, a sector encompassing significant AI endeavors, received a median salary of approximately USD 140,910 in 2024, with employment anticipated to increase by roughly 20 percent over the next ten years, significantly outpacing the average profession (U.S. Bureau of Labor Statistics, 2025).

The World Economic Forum has projected that the need for specialists in artificial intelligence and machine learning may increase by approximately 40 percent over a recent multi-year period, resulting in the creation of around one million new positions (Coursera, 2025b).

The expertise delineates the premium. An adept NLP engineer integrates programming, typically in Python, with a solid comprehension of machine learning, statistics, and sufficient linguistic knowledge to grasp the complexities of language.

They gather and sanitize data, construct models, and assess outcomes using metrics like the F1 score for classification or the BLEU score for translation.

The combination of engineering, mathematics, and linguistic intuition is indeed uncommon, and such scarcity warrants a premium.

Language is not one problem; it is thousands

If instructing a machine in language were a singular endeavor, the discipline would have concluded long ago. It is not a singular endeavor, as human language is perpetually elusive.

The identical term possesses various interpretations depending on the context. Sarcasm distorts the explicit meaning. Colloquial expressions evolve on a monthly basis. Individuals frequently err in spelling, utilize abbreviations, and amalgamate languages within a single sentence.

In Indonesia, this prevalent practice known as code mixing is ubiquitous: a solitary social media entry may intricately combine Bahasa Indonesia, English, and a regional dialect, occasionally in manners that even proficient speakers of the original local language can comprehend only vaguely. This phenomenon is the reason the engineer's work is never truly complete.

A model developed for formal journalism falters in informal conversation. A system designed for a specific dialect misinterprets another. Every novel domain, tone, or demographic introduces new uncertainties that require instruction.

Instructing a machine in language resembles nurturing a garden that perpetually produces new weeds rather than merely installing software.

The Indonesian angle: why Bahasa Indonesia is both hard and important

For engineers engaged in the Indonesian market, the difficulties are more pronounced than the global norm, which is precisely what renders the work captivating.

Indonesian ranks among the most extensively spoken languages globally. It frequently ranks around tenth in terms of global language prevalence, boasting nearly 200 million speakers, and is one of the most utilized languages on the internet (Koto et al., 2020).

For an extended period, it received inadequate attention from NLP research. In contrast to English, there existed a scarcity of annotated datasets, a limited number of pretrained models, and minimal standardization. In technical jargon, Indonesian was a linguistically deficient language despite having a vast speaker base.

The difference began to lessen through deliberate effort. Institut Teknologi Bandung, Universitas Indonesia, Gojek, and Prosa all worked together to create the IndoNLU benchmark. AI introduced the initial comprehensive array of Indonesian language comprehension tasks alongside the IndoBERT models, which were trained on an Indonesian corpus of approximately four billion words referred to as Indo4B (Wilie et al., 2020).

A concurrent initiative resulted in the creation of IndoLEM and an independent IndoBERT for additional tasks (Koto et al., 2020). These resources enable Indonesian systems to achieve a standard that global multilingual models could not provide on their own.

The profound challenge extends beyond the official language. Indonesia ranks as the second most linguistically diverse nation globally, boasting over 700 spoken languages distributed across an archipelago of more than seventeen thousand islands (Aji et al., 2022).

The majority of these languages possess minimal digital text resources for study, and numerous are classified as endangered. Scholars have begun to tackle this issue directly, exemplified by NusaX, a complementary dataset encompassing ten regional Indonesian languages, including Javanese, Sundanese, Minangkabau, and Balinese (Winata et al., 2023).

For a natural language processing engineer, the task represents a new frontier. Instructing a machine in Bahasa Indonesia is a well-defined issue at a high level but remains unresolved in finer aspects; conversely, educating it in Acehnese or Toba Batak represents truly groundbreaking research.

The organization spearheading this advancement, IndoNLP, articulates its objective clearly as enhancing the quality of Indonesian language technology (IndoNLP, n.d.). Investigations into the historical development of the field reveal the significant evolution of the local research foundation from rudimentary stemming and part-of-speech instruments to contemporary transformer models (Amien, 2023).


Where you see this work in daily Indonesian life

The benefits of this educational initiative are already evident throughout Indonesia.

The nation ranks among the most expansive digital markets globally, boasting approximately 229 million internet users and a digital economy nearing one hundred billion United States dollars annually, projected to reach significantly higher amounts by 2030 (Digital in Asia, 2026; Business Indonesia, 2025).

Language technology is integral to that expansion. Generative assistants have been embraced with remarkable rapidity: one assessment indicated that ChatGPT constituted nearly 76 percent of AI chatbot utilization in Indonesia in April 2025 (Databoks, 2025), and Indonesia has consistently been listed among the largest user bases for these technologies globally. Enterprises have adhered.

Financial institutions utilize Indonesian-language aids for customer support, online retail platforms provide 24/7 assistance to consumers, and transportation and payment super-applications rely on linguistic systems to manage extensive operations.

A comprehensive analysis of Indonesian chatbots from 2021 to 2025 documented the transition from basic rule-based frameworks to machine learning methodologies that integrate models like IndoBERT with contemporary language models, while highlighting that achieving accuracy in Indonesian continues to be a significant challenge (Susanto et al., 2026).

The business potential is growing correspondingly, with projections estimating the Indonesian market for AI-driven chatbots to reach approximately USD 6.8 billion by 2025 and escalate to around USD 26.4 billion by 2031 (Mobility Foresights, 2025). Adoption is inconsistent, primarily focused in Jakarta and the broader Java area, presenting significant opportunities for engineers to develop systems that function effectively outside the capital (Introl, 2025).


How NLP engineers actually teach a machine

The daily creation adheres to a discernible trajectory. It commences with information.

An engineer collects pertinent text or speech related to the issue, subsequently refines it due to the prevalence of noise in unprocessed language, and frequently annotates it by labeling instances for the model's learning.

Subsequently, representation involves converting that language into tokens and embeddings. Subsequently, the model undergoes training, or more frequently, a pretrained model is refined for the particular task and domain, resulting in significant savings in time and computational expenses. Subsequent to training, the engineer assesses the outcomes against reserved examples employing explicit metrics, as a model that appears remarkable in a demonstration may underperform discreetly in a production environment.

The system is deployed only at that point, and even after deployment, efforts persist through monitoring, as language continually evolves and models may deviate.

Humans remain engaged, monitoring results, rectifying mistakes, and reintegrating those amendments. Instructing a machine in a language is inherently a repetitive process.

Studying NLP and AI in Jakarta

For students in Indonesia who find this compelling, the field is unusually welcoming to people who sit between disciplines, and that is where a design- and business-focused school has a natural advantage.

At Raffles Jakarta, on Jalan M.H. Thamrin in the center of the capital, the artificial intelligence program gives students the technical grounding in machine learning and language technology while placing it inside a wider creative and commercial context.

Language work rewards more than code. An understanding of human cognition, which the psychology program develops, helps explain why people phrase things the way they do.

The business administration and the one-year English language MBA tracks connect language models to the customer analytics and decision-making that give them commercial value.

Digital Media Design and Visual Communication Design and train students to develop the interface sense needed to turn a raw model into something people actually enjoy using.

Studying in Jakarta adds a further edge that no overseas campus can match: direct exposure to the Indonesian market, its languages, and its users, which is precisely the context where the next wave of local language technology will be built.


Frequently Asked Questions

What does an NLP engineer do? An NLP engineer builds systems that let computers understand and produce human language. Their work includes collecting and cleaning text data, converting language into numeric form, training or finetuning models, evaluating results with metrics such as F1 or BLEU, and deploying tools like chatbots, translators, search engines, and sentiment analyzers into real products.

Why do machines need to be taught language at all? Machines need to be taught language because they process numbers, not meaning. Human language is unstructured and ambiguous, so an engineer must translate words into mathematical representations a model can learn from, then translate the model's output back into readable language. Without that work, a computer cannot reliably read a sentence or answer a question.

Is natural language processing a promising career in 2026? Natural language processing is a strong career choice. The global NLP market is projected to grow toward roughly USD 439.85 billion by 2030; average pay for NLP engineers sits well above typical software salaries, and demand for AI specialists is rising far faster than the average occupation, according to labor market data (Grand View Research, 2025; U.S. Bureau of Labor Statistics, 2025).

Why is Bahasa Indonesia difficult for NLP? Bahasa Indonesia is difficult for NLP because it was historically resource-poor, with few annotated datasets and pretrained models, despite having close to 200 million speakers. Frequent code mixing with English and regional languages, along with informal online writing, adds further complexity. Projects such as IndoNLU and IndoBERT have improved the situation substantially (Wilie et al., 2020).

How many languages does Indonesia have, and why does that matter for NLP? Indonesia has more than 700 living languages, making it the second most linguistically diverse country in the world (Aji et al., 2022). This matters because most of these languages have very little digital text, so building language technology for them is pioneering and difficult work, and it is a major opportunity for engineers who take it on.

Where can I study artificial intelligence and NLP in Jakarta? You can study artificial intelligence in Jakarta at Raffles Jakarta, located on Jalan M.H. Thamrin in Central Jakarta. The artificial intelligence program combines machine learning and language technology with the school's design and business disciplines, and its location gives students direct access to the Indonesian market where local language technology is being built.


Marketing Manager



References

Aji, A. F., Winata, G. I., Koto, F., Cahyawijaya, S., Romadhony, A., Mahendra, R., Kurniawan, K., Moeljadi, D., Prasojo, R. E., Baldwin, T., Lau, J. H., & Ruder, S. (2022). One country, 700+ languages: NLP challenges for underrepresented languages and dialects in Indonesia. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (pp. 7226 to 7249). Association for Computational Linguistics. https://aclanthology.org/2022.acl-long.500/

Amien, M. (2023). Sejarah dan perkembangan teknik natural language processing (NLP) Bahasa Indonesia. arXiv. https://arxiv.org/abs/2304.02746

Business Indonesia. (2025). AI to shape Indonesia's digital economy as it moves toward USD 180 billion by 2030. https://business-indonesia.org/news/ai-to-shape-indonesia-s-digital-economy-as-it-moves-toward-usd-180-billion-2030

Coursera. (2025a). NLP engineer salary: Your 2026 guide. https://www.coursera.org/articles/nlp-engineer-salary

Coursera. (2025b). Machine learning salary: A 2026 guide. https://www.coursera.org/articles/machine-learning-salary

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pretraining of deep bidirectional transformers for language understanding. arXiv. https://arxiv.org/abs/1810.04805

Digital in Asia. (2026). Indonesia's digital economy in 2026: Mobile, e-commerce, and AI. https://digitalinasia.com/indonesia-hub/

Fortune Business Insights. (2025). Natural language processing (NLP) market size, share, and growth [2034]. https://www.fortunebusinessinsights.com/industry-reports/natural-language-processing-nlp-market-101933

Glassdoor. (2026). NLP engineer: Average salary and pay trends 2026. https://www.glassdoor.com/Salaries/nlp-engineer-salary-SRCH_KO0,12.htm

Grand View Research. (2025). Natural language processing market size, share, and trends analysis report. https://www.grandviewresearch.com/industry-analysis/natural-language-processing-market-report

IndoNLP. (n.d.). IndoNLP: Advancing Indonesian language NLP research. https://indonlp.github.io/

Introl. (2025). Indonesia AI: 92% adoption, USD 10.88B market by 2030. https://introl.com/blog/indonesia-ai-revolution-infrastructure-investment-2025

Koto, F., Rahimi, A., Lau, J. H., & Baldwin, T. (2020). IndoLEM and IndoBERT: A benchmark dataset and pretrained language model for Indonesian NLP. arXiv. https://arxiv.org/abs/2011.00677

MarketsandMarkets. (2026). Natural language processing (NLP) market report. https://www.marketsandmarkets.com/Market-Reports/natural-language-processing-nlp-825.html

Mobility Foresights. (2025). Indonesia AI-powered chatbot market size and forecasts 2031. https://mobilityforesights.com/product/indonesia-ai-powered-chatbots-market

Susanto and colleagues. (2026). Mapping the evolution of AI chatbots in Indonesia (2021 to 2025): A PRISMA-based systematic literature review on applications, technologies, and impacts. Engineering, Mathematics and Computer Science Journal. https://journal.binus.ac.id/index.php/EMACS/article/view/14942

U.S. Bureau of Labor Statistics. (2025). Computer and information research scientists. Occupational Outlook Handbook. https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. arXiv. https://arxiv.org/abs/1706.03762

Wilie, B., Vincentio, K., Winata, G. I., Cahyawijaya, S., Li, X., Lim, Z. Y., Soleman, S., Mahendra, R., Fung, P., Bahar, S., & Purwarianti, A. (2020). IndoNLU: Benchmark and resources for evaluating Indonesian natural language understanding. arXiv. https://arxiv.org/abs/2009.05387

Winata, G. I., Aji, A. F., Cahyawijaya, S., Mahendra, R., Koto, F., Romadhony, A., Kurniawan, K., Moeljadi, D., Prasojo, R. E., Fung, P., Baldwin, T., Lau, J. H., Sennrich, R., & Ruder, S. (2023). NusaX: Multilingual parallel sentiment dataset for 10 Indonesian local languages. arXiv. https://arxiv.org/abs/2205.15960

Li, X. (2020). IndoNLU: A benchmark for Bahasa Indonesia NLP. Gojek Product and Tech. https://www.gojek.io/blog/indonlu-a-benchmark-for-bahasa-indonesia-nlp

bottom of page