Machine Learning Engineer — Training Optimization

📋 Description

• Optimize large-scale model training pipelines (throughput, stability, cost)
• Improve distributed training strategies (data, model, and pipeline parallelism)
• Tune optimizers, schedulers, batch sizing, and precision (bf16 / fp16 / fp8)
• Reduce training time and compute cost via profiling and bottleneck analysis
• Collaborate with researchers on architecture-aware training strategies
• Build and maintain robust training infrastructure (checkpointing, fault tolerance, reproducibility)

🎯 Requirements

• Strong experience training large neural networks (LLMs or similarly large models)
• Hands-on experience with training optimization (not just model usage)
• Backpropagation, optimization algorithms, and training dynamics
• Distributed systems for ML training
• Experience with PyTorch (required)
• Comfort working close to hardware (GPUs, memory, networking constraints)

🎁 Benefits

• Experience with large-scale distributed training (multi-node, multi-GPU)
• Familiarity with DeepSpeed, FSDP, Megatron, or custom training stacks
• Experience optimizing training on AMD or NVIDIA GPUs
• Contributions to open-source ML infrastructure or research codebases
• Exposure to non-Transformer architectures (RNNs, hybrid models, etc.)