Senior Applied Voice AI Research Engineer

Job Description

Senior Applied AI Research Engineer\n

Voicegain · Fully remote · Compensation competitive for your market

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Voicegain builds speech-to-text and voice AI infrastructure. Our ASR runs inside production telephony systems — IVRs, contact centers, voice bots — where the audio is noisy, callers don’t follow the script, and “works on the benchmark” is not good enough.

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We’re hiring one person: a researcher who ships.

The problems\n

You’ll work at the intersection of LLMs and speech recognition, where many of the interesting failures live. A few examples of what “solved” looks like here:

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·     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.

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·     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.

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·     All of it working in Spanish, French, Polish, and other languages — not just English.

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There is no established playbook for much of this. You’ll help write it.

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·     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.

How we work\n

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.

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You work directly with the founders and have a short path from a successful experiment to a production implementation.

You\n

·      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. A PhD in ML / NLP / speech, or research experience of that depth, is a strong plus.

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·      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.

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·      Serious depth in speech or NLP. ASR, spoken-language understanding, semantic similarity, information extraction — you’ve done real work in at least one.

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·      A real engineer. Excellent Python. You can navigate a large Java or C++ codebase and ship production code, not just notebooks.

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·      Comfortable with production ML infrastructure. GPU inference, Kubernetes, model serving. Experience with the NVIDIA stack (NeMo, Riva, Triton, TensorRT) is a plus.

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·      A highly effective user of modern AI development tools — with the judgment to validate what they produce.

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·      Independent. You make sound technical decisions with minimal supervision and can explain and defend them.

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We don’t care whether you’ve worked on exactly these applications before. We care whether you have the depth, breadth, and curiosity to attack problems whose answers aren’t known yet.

Apply\n

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• Skip the cover letter. Send your CV plus a short note about the hardest applied-ML problem you’ve personally solved — what made it hard, how you approached it, and what you shipped
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