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Staff Machine Learning Engineer, Voice AI

Together AI· Engineering
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San Francisco, CA$220k - $280kfull-timePosted 5mo ago

About the role

Staff ML Engineer focused on building and optimizing the model serving stack for real-time voice AI workloads, including STT, TTS, and speech-to-speech, with hands-on work on inference engines and collaboration with model partners to bring production-grade voice models to scale.

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; set the performance bar others in the industry measure against, not just catch up to.
  • 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; naturalness, latency, and pronunciation fidelity for TTS; establish the internal benchmark methodology that informs model selection and roadmap decisions.
  • 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.
  • Diagnose and resolve the hardest performance problems in the stack — conduct systematic profiling and root-cause analysis from GPU kernel behavior to framework-level bottlenecks; 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; your technical decisions should raise the ceiling for the whole team.
  • Define and scale voice fine-tuning capabilities — lead the technical direction for enabling customers to fine-tune STT and TTS models on Together's infrastructure, establishing the primitives for differentiated voice experiences.
  • Lay technical foundations for a category-defining product surface — architect systems with enough foresight that they support multiple new voice products with minimal rework; think in terms of platforms, not point solutions.

Qualifications

  • 8+ years of ML engineering experience, with a demonstrated focus on model serving, inference optimization, or ML infrastructure at production scale — including systems you've owned from design through live traffic.
  • 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 don't stop at the API surface.
  • Expert-level Python and PyTorch proficiency, with a strong command of GPU optimization — CUDA kernels, memory hierarchies, profiling toolchains — and a track record of turning that knowledge into shipped latency or throughput wins.
  • Proven system design judgment — you've made architectural decisions that held up at scale and influenced how a team or platform evolved; you can articulate the tradeoffs you made and why.
  • Strong technical leadership — you operate with high autonomy, define the right problems before solving them, and raise the bar for engineering quality around you without requiring process overhead.
  • Sharp product intuition for developer tooling — you understand what voice application developers actually need to ship great products, and you let that shape your technical priorities, not just the other way around.
  • Proven ability to move fast in ambiguous environments — you've thrived on early-stage or platform teams where scope is wide, ownership is deep, and the roadmap you build is the one you execute.
  • Strong foundation in speech and audio ML (ASR/TTS architectures, audio signal processing) — directly relevant experience is strongly preferred; exceptional ML engineering fundamentals with genuine curiosity about the domain is also considered.
  • Familiarity with audio codec and tokenization schemes (SNAC, Encodec, DAC) is a meaningful plus at this level.
  • 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.

Skills

Required:Model serving and inference optimizationLLM serving enginesPythonPyTorch

Benefits

  • startup equity
  • health insurance and other competitive benefits
Last updated Sep 9, 2026. Interested? Apply on the company site.

About Together AI

The AI Acceleration Cloud - Fast Inference, Fine-Tuning & Training

Run and fine-tune generative AI models with simple APIs and scalable GPU clusters. Train & deploy at scale on The AI Acceleration Cloud.

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