Salient is an AI workflow automation tool for lenders. We're working with the largest auto lenders in America and are hiring!
About the Role:
We’re looking for an Applied Research Scientist with expertise in Text-to-Speech (TTS) to help us push the boundaries of speech synthesis. You’ll work on developing high-quality, low-latency TTS systems that power real-world applications. The ideal candidate combines deep modeling knowledge with a strong engineering mindset to deliver robust, scalable solutions.
Responsibilities:
- Develop and improve SOTA TTS models
- Optimize TTS systems for low-latency real-time inference, voice consistency, and prosody control
- Contribute to internal tooling for data processing, model training, and inference benchmarking
- Perform any relevant engineering tasks related to model training and serving. E.g., data ingestion, data cleaning, evaluation
Requirements:
- Proven track record developing SOTA TTS systems, or a PhD in speech synthesis
- Experience with large-scale training, audio preprocessing, and distributed training
Nice to Have:
- Familiarity with multilingual or code-switched TTS
- Experience with voice cloning, style transfer, or emotion conditioning in speech
- Contributions to academic publications or open-source projects in speech generation
About Salient
Salient is building the future of consumer loan servicing, starting by transforming the auto lending industry. Since our September 2023 launch, we've:
- Scaled to more than $10M in ARR
- Partnered with some of the largest consumer lenders in America
- Cash flow positive
- Raised $65m in funding
- Interfaced with more than 2 million unique US consumers
- Processed over $150M in cash transactions
- Prevented $30M in fraud
- Identified 35k+ CFPB violations
- In-person office culture in San Francisco, CA
Backed by top-tier investors including Andreessen Horowitz, Matrix, General Catalyst, and Y Combinator.
Please also apply here: https://jobs.ashbyhq.com/salient/84831b8e-b09a-491c-90a0-d604a2a95f0d?utm_source=jWxe7rB9Q6
python + LLM stack + transformer based voice models
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