Hiring: Staff Machine Learning Engineer, Voice AI Company: Together AI Location: San Francisco, CA Job Posted Time: 2026-09-10 10:29:34 Employment Type: Full-time Target Skills & Keywords : Fine-tuning, LLM, PyTorch, Python, Reinforcement Learning, Salesforce, Serverless, Systems Design, TensorRT, Zoom, vLLM About the job Experience: •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. Required Skills: •Together AI is building the best inference infrastructure for voice applications. 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. •Own the model serving stack that powers Together's voice platform across STT, TTS, and speech-to-speech. •Work directly with state-of-the-art accelerators (H100s, H200s, B200s) to optimize voice model inference. •Partner cross-functionally with model partners (Cartesia, Deepgram, Rime, and others) to bring their models to production on Together's infrastructure. •Build quality evaluation frameworks that guide model selection for customers and inform the roadmap. •Join a small, early-stage team with outsized impact on a fast-growing product area. •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. Qualifications: •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. •Operational familiarity with audio codec and tokenization schemes (SNAC, Encodec, DAC) is a meaningful plus at this level. •Bachelor's or Master's in Computer Science, Electrical Engineering, or related field — or equivalent depth demonstrated through your work. Compensation: •$220,000 - $280,000 / year •Competitive benefits and rewards package •We offer competitive compensation, startup equity, health insurance and other competitive benefits Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!