Machine Learning Engineer

Responsibilities

• Optimize large-scale model training pipelines for throughput, convergence, stability, and cost
• Improve distributed training strategies including 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 for checkpointing and fault tolerance
• Evaluate and integrate new training techniques like ZeRO, FSDP, and custom kernels
• Own training performance metrics and continuously push them forward

Requirements

• Strong experience training large neural networks (LLMs or similar)
• Hands-on experience with training optimization
• Solid understanding of backpropagation, optimization algorithms, and training dynamics
• Knowledge of distributed systems for ML training
• Proficiency with PyTorch
• Comfort working close to hardware (GPUs, memory, networking constraints)
• Ability to move between research ideas and production-ready code

Preferred Qualifications

• 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

About the Company

We are a Series-A stage company building cutting-edge models and training systems at scale with a small, highly technical team.