Staff Machine Learning Engineer, Voice AI

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
• 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
• Build the voice evaluation platform — design a rigorous evaluation framework covering WER across accents, languages, and noise conditions for STT; naturalness, latency, and pronunciation fidelity for TTS
• Shape the architecture for next-generation model support — anticipate and enable emerging model paradigms (audio-native LLMs, codec-based architectures, end-to-end speech-to-speech systems)
• Serve as the technical DRI for model partner integrations — lead collaboration with partners such as Cartesia, Deepgram, and Rime
• Diagnose and resolve performance problems — conduct systematic profiling and root-cause analysis from GPU kernel behavior to framework-level bottlenecks
• Influence platform architecture — partner with platform engineering leadership to ensure the serving layer meets latency and reliability demands of real-time voice APIs
• Define and scale voice fine-tuning capabilities — lead technical direction for enabling customers to fine-tune STT and TTS models on Together's infrastructure

Requirements

• 8+ years of ML engineering experience with focus on model serving, inference optimization, or ML infrastructure at production scale
• Deep expertise in LLM serving engines (vLLM, SGLang, TensorRT-LLM)
• Expert-level Python and PyTorch proficiency with strong command of GPU optimization (CUDA kernels, memory hierarchies, profiling toolchains)
• Proven system design judgment and architectural decisions that held up at scale
• Strong technical leadership with high autonomy
• Sharp product intuition for developer tooling
• Strong foundation in speech and audio ML (ASR/TTS architectures, audio signal processing) preferred
• Familiarity with audio codec and tokenization schemes (SNAC, Encodec, DAC) is a plus
• Experience training or fine-tuning speech models at scale is an advantage
• Bachelor's or Master's in Computer Science, Electrical Engineering, or related field

Nice-to-Haves

• Experience modifying engine internals and contributing improvements back to serving frameworks
• Proven ability to move fast in ambiguous, early-stage environments