ML Research Engineer (Distributed Training)
Confirmed on the employer's own hiring board on Sep 24, 2026. First seen by Alion on Mar 26, 2026. METAMORPHIC scores B on the Alion truth index.
About Metamorphic
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'll 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
We offer:
The chance to work on one of the most scientifically consequential AI projects being pursued today
A small, world-class team where your contributions directly shape the science and the company
Competitive compensation and benefits, along with visa sponsorship
Strong mentorship and career development
Salary Range
$200,000 - $280,000 USD
Based on experience. We additionally offer a competitive equity package and comprehensive benefits, as well as visa sponsorship for international candidates.
Minimum Qualifications
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 Have
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
We encourage you to apply even if you do not believe you meet every single qualification. If you don't see a role that fits, we encourage you to submit a general application and tell us how you'd like to contribute to our mission.