Together AI
Staff Machine Learning Engineer, Voice AI
San Francisco · Staff+ · Full-time
Sponsorship not specified$220k-$280kDetected 63 days ago
PythonAlgorithmsPlatform EngineeringMachine LearningPyTorchLLMsElectrical EngineeringSignal ProcessingResearchLeadershipCollaboration
About the role
- Our Voice AI platform powers production-grade, real-time voice agents and applications - serving speech-to-text and text-to-speech models with best-in-class latency and reliability.
- This is a foundational hire on a small, high-impact team.
- Voice inference has unique challenges - streaming audio, tokenization, real-time latency budgets - that require dedicated ML engineering focus.
Responsibilities
- Own the voice inference roadmap end-to-end - define and execute the technical strategy for optimizing STT, TTS, and speech-to-speech models across Together's infrastructure, with a clear-eyed view of where the field is heading and how to position the platform ahead of it.
- Drive best-in-class inference performance - architect and implement systems targeting leading TTFB, throughput, and GPU utilization for voice workloads
- Lead productionization of voice models at scale - design the serving architecture for serverless and dedicated endpoints, including batching strategies, streaming inference pipelines, and memory management tailored to real-time audio
- own reliability and latency SLAs.
- Build the voice evaluation platform - design a rigorous, extensible evaluation framework covering WER across accents, languages, and noise conditions for STT
- Shape the architecture for next-generation model support - anticipate and enable emerging model paradigms - audio-native LLMs, codec-based architectures (SNAC, Encodec), and end-to-end speech-to-speech systems - before they're mainstream, not after.
- Serve as the technical DRI for model partner integrations - lead deep collaboration with partners such as Cartesia, Deepgram, and Rime
- own the full lifecycle from integration to optimization to ongoing performance accountability.
- drive shipped improvements with documented, measurable impact.
- Influence platform architecture across the organization - partner with platform engineering leadership to ensure the serving layer is built for the latency and reliability demands of real-time voice APIs
Requirements
- Deep, practical expertise in LLM serving engines (vLLM, SGLang, TensorRT-LLM, or equivalent) - you've modified engine internals, debugged edge cases under load, and contributed improvements back
- you can articulate the tradeoffs you made and why.
- Experience training or fine-tuning speech models at scale is a significant advantage.
- Bachelor's or Master's in Computer Science, Electrical Engineering, or related field - or equivalent depth demonstrated through your work.
Nice to have
- Strong foundation in speech and audio ML (ASR/TTS architectures, audio signal processing) - directly relevant experience is strongly preferred
- Familiarity with audio codec and tokenization schemes (SNAC, Encodec, DAC) is a meaningful plus at this level.
- exceptional ML engineering fundamentals with genuine curiosity about the domain is also considered.
Compensation
- We offer competitive compensation, startup equity, health insurance and other competitive benefits.
- The US base salary range for this full-time position is: $220,000 - $280,000 + equity + benefits.
- Our salary ranges are determined by location, level and role.
- Individual compensation will be determined by experience, skills, and job-related knowledge.
Equal opportunity
- Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.
Visa & Work Authorization
- t opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more
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This listing is sourced directly from Together AI's careers page and normalized into a canonical job model.