Senior Machine Learning Engineer | Together AI · Teeming.ai
Together AI
Together AI is a research-driven artificial intelligence company. We contribute leading open-source research, models, and datasets to advance the frontier of AI. Our decentralized cloud services…
Together AI is a research-driven artificial intelligence company. We contribute leading open-source research, models, and datasets to advance the frontier of AI. Our decentralized cloud services…
Series B co-led by Prosperity7; reported valuation at $3.3B in coverage
Investor Signal
“Participation from strategic investors including NVIDIA and Salesforce Ventures; Series B led by General Catalyst and co-led by Prosperity7”
Founders
What we do
Join the Team
Senior Machine Learning Engineer
On-SiteSan Francisco Bay Area, US
On-Site • San Francisco Bay Area, US
Related Companies
Company
HQ
Industry
Total Funding
Baseten
🇺🇸US
—
$585M
Luma
🇺🇸US
Data and AnalyticsDeepTechGamingHardwareInformation TechnologySoftware
-
Modular
🇺🇸US
Data and AnalyticsDeepTechInformation TechnologySoftware
$380M
WRITER
🇺🇸US
Data and AnalyticsDeepTechInformation TechnologyMediaSoftware
-
Physical Intelligence
🌍Remote
Data and AnalyticsDeepTechEducationHardwareSoftware
-
Who you are
5+ years of experience in ML engineering, with a focus on model serving, inference optimization, or ML infrastructure
Hands-on experience with LLM serving engines (vLLM, SGLang, TensorRT-LLM, or similar) — comfortable reading and modifying engine internals, not just using APIs
Strong proficiency in Python and PyTorch; experience with GPU profiling and optimization (CUDA, memory management, kernel-level debugging)
Track record of shipping ML systems to production with measurable performance improvements
Strong product sense — you think about what developers building voice apps actually need, not just what's technically interesting
Comfort working on a small, early-stage team where you'll wear multiple hats and move fast
Experience with speech and audio ML (ASR, TTS architectures, audio signal processing) is a strong plus but not required — you can learn this quickly if you have strong ML engineering fundamentals
Familiarity with audio codecs and tokenization schemes (SNAC, Encodec, DAC) is a plus
Experience training or fine-tuning speech models is a plus
Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field, or equivalent practical experience
What the job involves
Benefits
Competitive health insurance plans
Dental and vision insurance
Pre-tax flexible spending accounts
Mental health support and services
Income protection & retirement
401(k) plan
AD&D insurance
Startup jobs. A lot of them.
Your next opportunity is in here somewhere. Sign up to explore 70,000+ startups and their open roles. No spam. No gamification. Just jobs.
70,000+
Startups
83,000+
Open Roles
4,800+
New This Week
Frontend Developer
Part-timeJerusalem
Part-time • Jerusalem
Data Scientist
InternshipCambridge, GB
Internship • Cambridge, GB
Mobile Developer
ContractAmsterdam, NL
Contract • Amsterdam, NL
AI Researcher
Full-timeSan Francisco, US
Full-time • San Francisco, US
DevOps Engineer
Part-timeMunich, DE
Part-time • Munich, DE
Data Scientist
Part-timeAustin, US
Part-time • Austin, US
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
We're looking for a Senior ML Engineer to drive the model serving layer for voice workloads
You'll work hands-on with inference engines like TRT-LLM and SGLang to optimize how we serve models like Whisper, Parakeet, Orpheus, and Kokoro — pushing latency and throughput to the frontier
You'll profile GPU utilization, design batching strategies for streaming audio, and ensure new model architectures can go from research to production quickly
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
You'll shape how Together serves voice models as the industry moves from pipeline architectures (ASR → LLM → TTS) toward end-to-end speech-to-speech
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
Collaborate 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
Optimize inference performance for voice models (STT, TTS, speech-to-speech) — targeting best-in-class TTFB, throughput, and GPU utilization across our curated model set
Productionize voice models on serverless and dedicated endpoints, including batching strategies, streaming inference, and memory management tailored to audio workloads
Build and maintain a voice model evaluation framework — measuring WER across accents, languages, and noise conditions for STT; naturalness, latency, and pronunciation accuracy for TTS
Enable new model architectures in our serving stack as the field evolves, including audio-native LLMs, codec-based models (SNAC), and speech-to-speech systems
Collaborate with model partners to integrate and optimize their models (Cartesia, Deepgram, Rime, and others) running on Together's infrastructure
Profile and debug performance across the full inference stack — from GPU kernels to framework-level bottlenecks — and ship measurable improvements
Work with the platform engineering side of the team to ensure the serving layer meets the latency and reliability requirements of real-time voice APIs
Contribute to voice model fine-tuning capabilities (STT and TTS) as we enable customers to build differentiated voice experiences on Together
Lay the groundwork for multiple new products down the line
