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About This Role
Position Overview We are seeking a Vietnamese Linguistic Specialist to support NLP and speech-technology projects involving grammar development, language localization, phonetics, pronunciation resources, tokenization, text normalization, and Vietnamese language variation. This is a technical linguistics position, not a translation-only or general annotation role. We welcome candidates from different backgrounds, including: Linguists or language specialists with an interest in technology and some experience using Linux, Generative AI tools, or technical language workflows. Candidates with experience in computational linguistics, NLP, language technology, speech technology, or a related technical field. Previous professional experience with every technology listed below is not required. Candidates should, however, be comfortable learning technical concepts, following structured specifications, analyzing language patterns, and completing a practical technical assessment. Responsibilities Develop, extend, test, and localize structured language grammars for Vietnamese. Review and adapt rule-based grammar formats such as JSGF, context-free grammars, BNF, or comparable frameworks. Create and evaluate Vietnamese utterance patterns containing intents, entities, slots, and alternative sentence structures. Identify issues related to grammar coverage, ambiguity, overgeneration, consistency, and scalability. Support the development and review of Vietnamese pronunciation dictionaries and other linguistic resources. Work with phonetic representations and grapheme-to-phoneme workflows. Contribute to tokenization, text preprocessing, and text-normalization processes. Analyze Vietnamese word boundaries, sentence structures, diacritics, abbreviations, slang, spelling variations, and conversational language. Identify and document differences among Northern, Central, and Southern Vietnamese varieties. Distinguish between formal written Vietnamese and spontaneous spoken Vietnamese. Develop and improve linguistic guidelines, normalization rules, annotation instructions, and technical documentation. Explain linguistic decisions, Vietnamese-specific challenges, and recommended solutions clearly. Collaborate with linguists, NLP engineers, speech-technology specialists, project managers, and other stakeholders. Qualifications Required Qualifications Fluent or native-level proficiency in Vietnamese. Professional working proficiency in English, including the ability to understand technical instructions and explain linguistic decisions in writing. A background in linguistics, language studies, computational linguistics, NLP, language technology, speech technology, or another relevant field. Strong knowledge of Vietnamese grammar, word boundaries, sentence structure, and diacritics. Familiarity with Vietnamese abbreviations, slang, spelling variations, and conversational language. Awareness of Northern, Central, and Southern Vietnamese language variation. Ability to distinguish between formal written Vietnamese and spontaneous spoken Vietnamese. Some familiarity with Linux environments and command-line tools. Familiarity with Generative AI tools or language-model applications. Strong analytical, problem-solving, and technical-writing skills. Ability to learn new technical concepts and work with structured grammar formats. Ability to work independently and follow detailed technical specifications consistently. Formal training in computational linguistics is not mandatory. Linguists and language specialists with the required Vietnamese expertise who are comfortable working with Linux, Generative AI, and structured technical tasks are encouraged to apply. Preferred Qualifications Experience with JSGF, context-free grammars, BNF, or another grammar-based language framework. Familiarity with NLP concepts such as intents, entities, slots, utterance patterns, tokenization, and normalization. Experience with phonetics, pronunciation dictionaries, or grapheme-to-phoneme conversion. Experience in ASR, NLU, conversational AI, TTS, or another speech-technology area. Familiarity with regular expressions, finite-state methods, or rule-based text processing. Experience developing linguistic datasets, annotation guidelines, normalization rules, or language-generation resources. Basic programming or scripting experience, preferably in Python. Experience evaluating linguistic resources for coverage, ambiguity, accuracy, consistency, and scalability. Conditions Technical Assessment As the first step in the recruitment process, selected candidates will be invited to complete a brief technical assessment designed to evaluate their ability to: Interpret and extend a structured language grammar. Represent new intents and entities within the grammar. Localize grammar rules and example utterances into Vietnamese. Identify Vietnamese-specific linguistic challenges. Explain how the proposed approach could be expanded to support additional utterances and linguistic variation. The assessment is intended to evaluate practical reasoning and learning ability. Previous professional experience with the exact grammar format used in the assessment is not required. Compensation Compensation will be negotiated based on the candidate’s qualifications, linguistic expertise, technical skills, and relevant professional experience. If you have strong Vietnamese linguistic knowledge and are comfortable working with language technology, Linux, and Generative AI tools, we invite you to apply. We appreciate all applicants' interest. We will contact only candidates selected for the next stage of the recruitment process.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Datavio.ai, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Datavio.ai AI Hiring
Datavio.ai has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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