Featherless AI
Machine Learning Engineer — Training Optimization
Remote Machine Learning Engineer role with clear candidate location fit.
PostedJul 25, 2026
Eligible countriesWorldwide
Seniority signalMiddle
Work settingRemote
Accepted candidate locations
Worldwide
Role overview
Machine Learning Engineer — Training Optimization
Requirements and responsibilities
Readable role content extracted into sections for faster review.
What You’ll Do
- 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
What We’re Looking For
- 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 dynamicsDistributed systems for ML training
- 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
Solid understanding of:
- Backpropagation, optimization algorithms, and training dynamics
- Distributed systems for ML training
Nice to haves
- 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.)
Why Join Us
- Real ownership at Series-A stage — your work shapes the company’s trajectory
- Work on cutting-edge models and training systems at scale
- Small, highly technical team with fast feedback loops
- Strong emphasis on engineering quality and research rigor
- Competitive compensation + meaningful equity
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Location eligibility
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