Machine Learning Engineer — Training Optimization

We’re looking for an ML Engineer focused on training optimization to help us scale and improve large-scale model training. You’ll work at the intersection of research and production, optimizing training pipelines for speed, stability, and cost—while collaborating closely with researchers pushing model architecture and capability forward.

This is a high-impact role with real ownership: your work directly affects how fast we can iterate, how large we can scale, and how efficiently we deploy new models.

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  Optimize large-scale model training pipelines (throughput, convergence, stability, and cost)

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  Improve distributed training strategies (data, model, and pipeline parallelism)

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  Tune optimizers, schedulers, batch sizing, and precision (bf16 / fp16 / fp8)

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  Reduce training time and compute cost via profiling, bottleneck analysis, and systems-level improvements

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  Collaborate with researchers on architecture-aware training strategies

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  Build and maintain robust training infrastructure (checkpointing, fault tolerance, reproducibility)

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  Evaluate and integrate new training techniques (e.g. gradient checkpointing, ZeRO, FSDP, custom kernels)

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  Own training performance metrics and continuously push them forward

• Strong experience training large neural networks (LLMs or similarly large models)

• Hands-on experience with training optimization (not just model usage)

• Solid understanding of:

  • Backpropagation, optimization algorithms, and training dynamics

  • Distributed systems for ML training

• Experience with PyTorch (required)

• Comfort working close to hardware (GPUs, memory, networking constraints)

• Ability to move fluidly between research ideas and production-ready code

• 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.)