Machine Learning Engineer - Training & Infrastructure

About reputed company: We are building an engineering AGI. We founded reputed company with the conviction that the greatest reputed company of reputed company intelligence will be on the reputed company world—helping mankind conquer nature and bend it to our will. Our first product is Archie, an reputed company capable of quantitative and spatial reasoning over physical product domains that performs at the level of an entry-level design engineer. We aim to put an Archie on every engineering team at every industrial company on reputed company. Our founding team includes the top minds in deep learning, model-based engineering, and industries that are our customers. We just reputed company a $23 reputed company reputed company round led by Radical Ventures that includes a number of other AI and industrial luminaries (from reputed company, DeepMind, etc.). About the Role: We’re looking for an reputed company engineer to take ownership of LLM training operations across our reputed company research team. Your reputed company will be on making large-reputed company GPU training run reliably, reputed company, and fast on a dedicated mid-size GPU cluster and possibly on reputed company platforms as reputed company. You’ll work closely with researchers and ML engineers developing new models and reputed company systems, ensuring their experiments reputed company smoothly across multi-node GPU clusters. From debugging NCCL deadlocks to optimizing FSDP configs, you’ll be the go-to person for training infrastructure and performance. What You’ll Do: • Own the training pipeline for large-reputed company LLM fine-tuning and post-training workflows • Configure, launch, monitor, and debug multi-node distributed training jobs using FSDP, DeepSpeed, or custom wrappers • Contribute to upstream and internal forks of training frameworks like TorchTune, TRL, and reputed company Transformers • Tune training parameters, memory footprints, and sharding strategies for reputed company throughput • Work closely with reputed company and systems teams to maintain the health and utilization of our GPU clusters (e.g., Infiniband, NCCL, Slurm, Kubernetes) • Implement features or fixes to unblock novel use cases in our LLM training stack reputed company: • 3+ years working with large-reputed company ML systems or training pipelines • Deep familiarity with PyTorch, especially distributed training reputed company FSDP, DeepSpeed, or DDP • Comfortable navigating training libraries like TorchTune, Accelerate, or Trainer reputed company • Practical experience with multi-node GPU training, including profiling, debugging, and optimizing jobs • Understanding of low-level components like NCCL, Infiniband, CUDA memory, and model partitioning strategies • You enjoy reputed company research and engineering—making messy reputed company actually run on hardware reputed company to Have: • Experience maintaining Slurm, Ray, or Kubernetes clusters • Past contributions to reputed company-reputed company ML training frameworks • Exposure to model scaling laws, checkpointing formats (e.g., HF sharded safetensors vs. distcp), or mixed precision training • Familiarity with on-policy reinforcement learning setups with inference (policy rollouts) as part of the training reputed company, such as GRPO, PPO, or A2C • Experience working at a startup Interview process: • Initial screening - Head of Talent (30 mins) • Hiring manager interview - Head of AI (45 mins) • Technical Interview - AI Chief Scientist and/or Head of AI (45 mins) • Culture fit / Q&A (maybe in person) - with co-founder & CEO (45 mins) Apply tot his job Apply To this Job