Speech AI Engineer

VINFAST is a pioneering electric vehicle (EV) company committed to revolutionizing the automotive industry with sustainable and innovative mobility solutions. As a leading player in the EV market, VinFast is dedicated to delivering high-quality, cutting-edge electric vehicles that redefine the driving experience. Our team consists of passionate professionals driven by a shared vision of creating a greener and more sustainable future through innovation, technology, and excellence.
The Speech & Conversational AI Center - Automotive AI Development Institute researches , develops, and deploys advanced Agentic AI and Voice AI solutions, applied in vehicles and extended to group-wide use cases such as robotics and AI assistants.
Today's voice agents can transcribe and speak, but they still perceive the world poorly - who is speaking, whether they're addressing the agent, how they feel, and what's happening around them.
Speech Understanding is our answer: a single, real-time, LLM-powered model that unifies these audio-understanding signals, so the Voice Agent reasons like a situationally aware partner, not just a command-taker. It outputs natural-language descriptions or structured probabilities the agent can act on directly. We start with Vietnamese and English, backed by our own data, benchmarks, and a shared evaluation framework across the ASR, TTS, Foundation, and Agent teams.
Responsibilities
Research and build a unified, real-time S peech U nderstanding model - powered by an LLM - that lets the Voice Agent perceive speakers, their state, the environment, and audio content beyond the transcript; prototype agent-awareness behaviors using simulated agents and scripted scenarios.
Build the Vietnamese data and benchmark foundations: data collection, annotation, and synthetic data, plus a shared evaluation framework that measures real impact on agent behavior, not only model metrics.
Train, fine-tune, and post-train audio and audio-LLM models (pretraining, SFT, alignment ) and optimize them for low-latency streaming inference.
Work with the ASR, TTS, Foundation, and Agent teams to integrate models into conversational systems and voice assistants, including demanding in-vehicle conditions.
Stay current with the research community and contribute new ideas and, where appropriate , publications.
In this role, you'll join the Speech Understanding team and work across the full loop - data, models, evaluation, and integration into a production Voice Agent. We're looking for people with relevant experience, passion, and drive, ready to learn fast and thrive under pressure.
Requirements
Bachelor's Degree or higher in Computer Science, Artificial Intelligence, or related fields.
Minimum 2 years of experience in AI/ML, with a focus on speech and audio (ASR, audio understanding, or related areas) or on audio/multimodal LLMs.
Proficient in Python and PyTorch, with a strong grasp of Transformers and modern speech/audio models (self-supervised encoders, Whisper-style models, audio-LLMs).
Hands-on experience training or fine-tuning models - ideally LLMs (SFT, PEFT/LoRA) - and building or evaluating speech datasets.
Comfortable with open-ended, research-driven problems where the problem definition is still evolving.
Good English proficiency to read technical documentation and research papers.
Preferred
Experience with Vietnamese, multilingual, or code-switching speech.
Experience with real-time streaming systems or voice agents, and interest in multimodal (voice and vision) agents.
Publications in top-tier AI/speech conferences (e.g., Interspeech, ICASSP, NeurIPS).
Benefits
Competitive salary
Premium healthcare package, including PVI insurance & annual health check-ups
13th-month salary & performance bonuses to reward your contributions
Enjoy preferential pricing for services within the Vingroup ecosystem including Vinmec, Vinpearl, and Vinschool...
Opportunity to collaborate with and learn from industry-leading professionals in the automotive domain
Work Location:
Technopark Tower, Vinhomes Ocean Park, Gia Lam, Hanoi, Vietnam
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