TA
$200,000 – $290,000
Listed on Together AI’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
Together AI seeks a Research Engineer to transform efficient training techniques from research into production systems that optimize large-scale model fine-tuning. The role suits engineers who combine deep ML systems knowledge with practical infrastructure work, partnering with researchers to profile, optimize, and deploy training workloads at scale.
Our summary, not Together AI’s wording. The full posting is on their site.
What they ask for
Required
- Independently manage performance and infrastructure problems from investigation to deployment
- Strong Python and PyTorch programming with emphasis on efficient, maintainable code
- Hands-on experience training or fine-tuning large neural networks in multi-GPU or multi-node environments
- Solid understanding of ML systems fundamentals including GPU architecture, mixed-precision training, and distributed training paradigms
- Strong communication and collaboration skills with researchers and engineers
- Stay current with AI research advances and apply them to real-world systems
Nice to have
- Experience writing optimized NVIDIA GPU kernels using CUDA or Triton or implementing communication collectives with NCCL or NVSHMEM
- Experience with large-scale training frameworks such as FSDP, DeepSpeed, Megatron-LM, or custom distributed training systems
- Experience optimizing distributed training for compute efficiency, memory efficiency, or scalability
- Experience running and managing large-scale GPU experiments including scheduling, monitoring, and fault tolerance
- Contributions to widely used open-source ML or ML systems projects
- Experience building or operating ML products or managed services used by external customers