Machine Learning Engineer - Training Optimization
Remote (world)Full-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- PyTorchDistributed Systems
Requirements
- 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
- Solid understanding of 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
Responsibilities
- Optimize large-scale model training pipelines (throughput, convergence, stability, and 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, bottleneck analysis, and systems-level improvements
- Collaborate with researchers on architecture-aware training strategies
- Build and maintain robust training infrastructure (checkpointing, fault tolerance, reproducibility)
- Evaluate and integrate new training techniques (e.g. gradient checkpointing, ZeRO, FSDP, custom kernels)
- Own training performance metrics and continuously push them forward
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