Research Engineer turning efficient foundation model training research into robust high-performance production systems at Together AI. Optimize large-scale training infrastructure, profile bottlenecks, integrate new models, and productionize novel methods in close partnership with scientists.
Research Engineer building platforms to customize open-source LLMs via fine-tuning, RL, and evaluation. Focus on integrating post-training with inference engines (vLLM, SGLang, TensorRT-LLM), optimizing for RL workloads, and ensuring production reliability. Requires 2+ years ML production experience and strong Python/Go skills.
Research intern on the Model Shaping team working on post-training methods, efficient neural network training, and foundation model evaluation. Requires strong ML fundamentals and PyTorch/JAX experience.
Intern developing and optimizing GPU-accelerated kernels for ML/AI applications. Requires strong GPU programming background (CUDA/Triton) and knowledge of performance optimization.
Staff ML Engineer to own the model serving stack for real-time voice inference (STT, TTS, speech-to-speech) on H100/H200 GPUs. Drive latency/throughput optimization using TRT-LLM and SGLang for models like Whisper and Parakeet.
Senior ML Engineer optimizes inference for voice AI models (STT, TTS, speech-to-speech) using engines like TensorRT-LLM and SGLang on GPUs. Requires 5+ years ML engineering with serving/inference expertise, Python/PyTorch proficiency, and production ML experience.
Research Engineer building production ML systems at the intersection of efficient inference, RL/post-training, and serving engines. Translates algorithms into scalable infrastructure improving latency, throughput, and model quality. Requires 3+ years ML systems experience and advanced degree.
Optimizes and builds production inference systems for large language models at scale using PyTorch and high-performance tooling. Requires 3+ years experience in production code, OS concepts, and AI inference systems.
Research Engineer turning efficient foundation model training research into robust high-performance production systems at Together AI. Optimize large-scale training infrastructure, profile bottlenecks, integrate new models, and productionize novel methods in close partnership with scientists.
Research Engineer building platforms to customize open-source LLMs via fine-tuning, RL, and evaluation. Focus on integrating post-training with inference engines (vLLM, SGLang, TensorRT-LLM), optimizing for RL workloads, and ensuring production reliability. Requires 2+ years ML production experience and strong Python/Go skills.
Research intern on the Model Shaping team working on post-training methods, efficient neural network training, and foundation model evaluation. Requires strong ML fundamentals and PyTorch/JAX experience.
Intern developing and optimizing GPU-accelerated kernels for ML/AI applications. Requires strong GPU programming background (CUDA/Triton) and knowledge of performance optimization.
Staff ML Engineer to own the model serving stack for real-time voice inference (STT, TTS, speech-to-speech) on H100/H200 GPUs. Drive latency/throughput optimization using TRT-LLM and SGLang for models like Whisper and Parakeet.
Senior ML Engineer optimizes inference for voice AI models (STT, TTS, speech-to-speech) using engines like TensorRT-LLM and SGLang on GPUs. Requires 5+ years ML engineering with serving/inference expertise, Python/PyTorch proficiency, and production ML experience.
Research Engineer building production ML systems at the intersection of efficient inference, RL/post-training, and serving engines. Translates algorithms into scalable infrastructure improving latency, throughput, and model quality. Requires 3+ years ML systems experience and advanced degree.
Optimizes and builds production inference systems for large language models at scale using PyTorch and high-performance tooling. Requires 3+ years experience in production code, OS concepts, and AI inference systems.