Research Engineer, Large-Scale Training

13 minutes ago
Full-time
Senior
Software Development
Together

Together

Together AI provides the fastest and most cost-efficient tools for building generative AI models, with a dedicated team of experts to support users in training their own models and advancing AI technology.

IT Services
1-10
$20M raised

Description

  • Design, implement, and optimize core components of Together's large-scale training infrastructure.
  • Integrate new model architectures and validate training correctness and convergence.
  • Optimize performance for production fine-tuning workloads.
  • Profile distributed training workloads and eliminate bottlenecks across compute, memory, and communication.
  • Design and execute experiments to validate performance hypotheses and benchmark new approaches.
  • Partner with Research Scientists to productionize novel training methods.
  • Contribute to publications and open-source releases.
  • Enable support for newly released open-source foundation models on the Together platform.
  • Build and maintain experimental infrastructure that is reliable and scalable.

Requirements

  • Demonstrated ability to take ambiguous performance or infrastructure problems from investigation through deployment.
  • Strong programming skills in Python and PyTorch.
  • Ability to write efficient, maintainable code.
  • Hands-on experience training or fine-tuning large neural networks in multi-GPU or multi-node environments.
  • Solid understanding of GPU architecture, mixed-precision training, and distributed training paradigms such as data, tensor, pipeline, or expert parallelism.
  • Strong communication skills and ability to collaborate with researchers and engineers.
  • Passion for staying current with advances in AI research and applying them to real-world systems.
  • Excitement about translating cutting-edge research into production systems that deliver customer impact.
  • Experience writing optimized NVIDIA GPU kernels using CUDA or Triton, or implementing communication collectives with NCCL or NVSHMEM (preferred).
  • Experience with FSDP, DeepSpeed, Megatron-LM, or custom distributed training systems (preferred).
  • Experience optimizing distributed training for compute efficiency, memory efficiency, or scalability (preferred).
  • Experience running and managing large-scale GPU experiments, including scheduling, monitoring, and fault tolerance (preferred).
  • Contributions to widely used open-source ML or ML systems projects (preferred).
  • Experience building or operating ML products or managed services used by external customers (preferred).

Benefits

  • Competitive compensation.
  • US base salary range of $200,000 - $290,000.
  • Startup equity.
  • Health insurance.
  • Other benefits.

Interested in this position?

Apply directly on the company website

Apply Now

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