Featherless AI
Machine Learning Engineer — Training Optimization
Vaga remota de Machine Learning Engineer com fit claro de localização do candidato.
Publicada25 de jul. de 2026
Países elegíveisGlobal
Sinal de senioridadeMiddle
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Global
Resumo da vaga
Machine Learning Engineer — Training Optimization
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
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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