Machine Learning Engineer — Training Optimization

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