Note: The job is a remote job and is open to candidates in USA. Voicegain builds speech-to-text and voice AI infrastructure for production telephony systems, including IVRs, contact centers, and voice bots. The Senior Applied AI Research Engineer will research and develop applied machine learning solutions at the intersection of LLMs and speech recognition, taking problems from formulation and experimentation through production implementation and live-traffic evaluation.
Responsibilities
- A redaction system that finds a credit card number in a transcript where the caller paused, corrected themselves, numbers were partially repeated by the agent, then corrected again
- AI grafted onto legacy ASR platforms: sometimes there’s no clean slate, and your AI has to live inside an existing stack — behind an MRCP interface, for example — without breaking what already works
- All of it working in Spanish, French, Polish, and other languages — not just English
- An ASR that takes incomplete or imperfect GRXML grammars and interprets callers’ utterances the way a human would. The grammar says •“pay bill”•; the caller says •“I’d like to pay off what I owe”•; the right semantic tag comes back anyway
- You get a problem, not a spec. You formulate it, read the papers, analyze deployment constraints, pick candidate approaches, prototype, measure on real data, analyze failures — then build the production version and watch how it behaves on live traffic
- You work directly with the founders and have a short path from a successful experiment to a production implementation
Skills
- Deep ML foundations. You understand how transformers, embeddings, and fine-tuning actually work — well enough to adapt or combine techniques when nothing off-the-shelf fits
- Hands-on with LLMs beyond prompting. You've fine-tuned models (LoRA/QLoRA or similar), evaluated them rigorously, and know when a small classifier beats an LLM
- Serious depth in speech or NLP. ASR, spoken-language understanding, semantic similarity, information extraction — you've done real work in at least one
- A real engineer. Excellent Python. You can navigate a large Java or C++ codebase and ship production code, not just notebooks
- Comfortable with production ML infrastructure. GPU inference, Kubernetes, model serving
- A highly effective user of modern AI development tools — with the judgment to validate what they produce
- Independent. You make sound technical decisions with minimal supervision and can explain and defend them
- A PhD in ML / NLP / speech, or research experience of that depth, is a strong plus
- Experience with the NVIDIA stack (NeMo, Riva, Triton, TensorRT) is a plus
Benefits
- Fully remote
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