机器学习工程师 — 训练优化
Machine Learning Engineer — Training Optimization
职位描述
我们正在寻找一名专注于训练优化的机器学习工程师，帮助我们扩展和提升大规模模型训练。你将在研究与生产之间工作，优化训练流程的速度、稳定性和成本——同时与研究人员紧密合作，推动模型架构和能力的发展。
这是一个影响深远的职位，拥有真正的主导权：你的工作将直接影响我们迭代的速度、扩展的规模以及新模型部署的效率。
你将负责
· 优化大规模模型训练流程（吞吐量、收敛性、稳定性及成本）
· 改进分布式训练策略（数据并行、模型并行和流水线并行）
· 调整优化器、调度器、批量大小和精度（bf16 / fp16 / fp8）
· 通过分析性能瓶颈和系统级改进来减少训练时间和计算成本
· 与研究人员合作开发面向架构的训练策略
· 构建和维护可靠的训练基础设施（检查点、容错和可复现性）
· 评估并集成新的训练技术（例如梯度检查点、ZeRO、FSDP、自定义内核）
· 主导训练性能指标并持续推动其提升
我们希望你具备
· 在训练大型神经网络（LLMs 或类似规模模型）方面有丰富经验
· 有训练优化的实际经验（不仅仅是模型使用）
· 熟悉以下内容：
· 反向传播、优化算法和训练动态
· 用于机器学习训练的分布式系统
· 有 PyTorch 使用经验（必须）
· 熟悉硬件层面的工作（GPU、内存、网络限制）
· 能够在研究想法和生产就绪代码之间灵活切换
加分项
· 有大规模分布式训练经验（多节点、多 GPU）
· 熟悉 DeepSpeed、FSDP、Megatron 或自定义训练框架
· 有在 AMD 或 NVIDIA GPU 上优化训练的经验
· 对开源机器学习基础设施或研究代码库有贡献
· 接触过非 Transformer 架构（RNN、混合模型等）
加入我们为什么
· 在 A 轮阶段拥有真正的主导权——你的工作将塑造公司的发展方向
· 在大规模上处理前沿模型和训练系统
· 小型、高度技术化的团队，反馈迅速
· 强调工程质量和研究严谨性
· 有竞争力的薪酬 + 有意义的股权
最初发布于 Himalayas
查看英文原文
About the Role
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.
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 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
Nice to Have
- 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
Originally posted on Himalayas