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Together AI is seeking a Research Engineer to join the Model Shaping team's Scaling group. This role bridges cutting-edge AI research and production systems, focusing on efficient foundation model training and inference.
You will design, implement, and optimize core components of Together's large-scale training infrastructure. Key responsibilities include integrating new model architectures, validating training correctness and convergence, and optimizing performance for production fine-tuning workloads. You'll profile distributed training workloads to identify and eliminate bottlenecks across compute, memory, and communication layers.
The role involves designing and executing experiments to validate performance hypotheses, benchmarking new approaches against state-of-the-art methods, and partnering closely with Research Scientists to productionize novel training techniques. You'll rapidly enable support for newly released open-source foundation models on the Together platform and build experimental infrastructure that accelerates research while maintaining production-quality reliability and scalability.
Required qualifications include demonstrated ability to independently tackle ambiguous performance and infrastructure problems from investigation through deployment. You need strong programming skills in Python and PyTorch with emphasis on efficient, maintainable code. Hands-on experience training or fine-tuning large neural networks in multi-GPU or multi-node environments is essential. You should have solid understanding of ML systems fundamentals including GPU architecture, mixed-precision training, and distributed training paradigms (data, tensor, pipeline, or expert parallelism). Strong communication and collaboration skills with both researchers and engineers are critical.
About Together AI
AI / Data / Infrastructure — cloud platform for open-source and generative AI model training and inference.
