ML Research Engineer (Distributed Training)

Metamorphic•Palo Alto, CA

About The Position

Metamorphic is developing new approaches to intelligence by combining machine learning with large-scale experimental neuroscience, informed by the principles that make the brain efficient, flexible, and robust. We are building foundation models trained on rich, continuous neural data — a high-resolution model of the brain at a scale never before possible. Our founding team spans machine learning, neuroscience, and neurotechnology, with prior work including the MICrONS project, Neuropixels, and the Enigma project, as well as foundational scientific contributions in learning, neural computation, and generative modeling. Our work sits at the frontier of AI research, and we believe the highest-impact discoveries will come from researchers and engineers working as a single, tightly collaborative team. The name Metamorphic reflects our belief that the next advances in intelligence will come from a change in form, beyond scale — from artificial to natural intelligence. About the Role We are hiring Research Engineers to join our growing AI research team. You will work on building and scaling the distributed systems that enable training Metamorphic’s state-of-the-art foundation models across thousands of GPU’s. This is a high-impact, technically deep role working at the frontier of ML research and engineering. You will design and optimize our distributed training framework, implement advanced parallelism strategies, build fault-tolerant infrastructure, and provide the tooling researchers need to run large-scale experiments quickly and reproducibly. You'll have substantial autonomy to shape foundational technical decisions on a small, high-impact team. You will thrive in this role if you: Have significant software engineering experience and can move quickly without sacrificing rigor Are able to balance research goals with practical engineering constraints Are happy to take on tasks outside your job description to support the team Enjoy pair programming and deeply collaborative work Are eager to learn more about machine learning research in a novel scientific domain Are enthusiastic to work at an organization that functions as a single, cohesive team pursuing large-scale AI research Have ambitious goals for AI progress and are excited to create the best outcomes over the long term

Requirements

Bachelor's degree or equivalent experience in Computer Science, Machine Learning, or a related field
Strong software engineering skills with a proven track record of building complex systems
Hands-on experience building and debugging distributed training infrastructure (PyTorch FSDP, DeepSpeed ZeRO, Megatron, TorchTitan, or similar) and optimizing advanced parallelism strategies
Strong understanding of GPU architecture and performance: memory hierarchy, tensor core utilization, bandwidth vs compute limitations
Strong understanding of the NVIDIA ecosystem: CUDA, NCCL, NVLink/NVSwitch topologies, mixed-precision training (MXFP8/NVFP4), and profiling tools
Deep familiarity with PyTorch internals, including torch.distributed, autograd, memory management, and torch.compile
Experience with cloud/HPC environments and job orchestration across hundreds of GPUs

Nice To Haves

Experience building fault-tolerant training pipelines, including checkpointing, automatic recovery, and infrastructure for reproducible experimentation
Experience with the latest in mixture-of-experts architectures, diffusion model training, or multimodal models
Experience with inference serving frameworks (vLLM, TensorRT-LLM) or building custom inference solutions

Responsibilities

Design and optimize our distributed training framework
Implement advanced parallelism strategies
Build fault-tolerant infrastructure
Provide the tooling researchers need to run large-scale experiments quickly and reproducibly