Machine Learning Engineer

We're looking for an MLE to scale the training and deployment of large transformer-based models. You'll work across training infrastructure, inference optimization, and reinforcement learning pipelines in multi-GPU and multi-node environments.

• Performance engineering of training, inference, and RL infrastructure for large language models
• Implementing parallelization strategies (data, tensor, pipeline, context) and optimizing bottlenecks
• Building fault-tolerant training systems with checkpointing and recovery
• Designing RL pipelines from reward modeling to policy optimization, including trainer-inference communication
• Verifying numerical correctness and validating methods from recent publications

• Experience in training and deploying large neural networks in production
• Expert-level PyTorch
• Multi-node, multi-GPU training and debugging experience
• Deep understanding of GPU memory management and distributed systems profiling
• Hands-on RL experience, including policy-space methods (GRPO, PPO, etc.)

• Multi-modal model training (e.g. separate vision encoders) and MoE / expert parallelism
• NVIDIA GPU programming (Triton, CUTLASS, custom CUDA kernels) and deep NCCL knowledge
• FP8 or FP4 training experience
• Familiarity with TorchTitan, SGLang, vLLM, Megatron, etc.
• Track record of open-source contributions