ML Research Engineer (Performance Engineering)

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.

We are seeking Research Engineers to join our growing AI research team. You will be responsible for maximizing the training and inference performance of Metamorphic's foundation models, from quantization and low-precision training, to MoE routing optimization, to writing custom CUDA/Triton kernels for our novel architecture. This is a high-impact, technically deep role at the frontier of ML research and engineering. You will write and optimize GPU kernels, profile and eliminate performance bottlenecks, tune low-precision training strategies, and work closely with researchers to ensure architectural decisions translate to efficient and scalable implementations. 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 able to turn theory and practice, translating paper ideas into robust and performant implementations.

• Get excited about the nitty gritty engineering details and incremental performance improvements that others gloss over.

• 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

$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.

• Bachelor's degree or higher in Computer Science, Machine Learning, or a related field

• Strong software engineering skills with a proven track record of building complex systems

• Strong proficiency in CUDA, Triton, or similar, with demonstrated experience writing and optimizing GPU kernels

• Hands-on experience with mixed-precision and low-precision training and a practical understanding of numerical stability tradeoffs

• Deep knowledge of transformer architectures at the implementation level

• Experience with MoE architectures: routing algorithms, load balancing, and the systems-level challenges of expert dispatch across GPUs

• Hands-on experience with GPU profiling tools (Nsight Compute, Nsight Systems, PyTorch Profiler)

• Experience integrating, customizing, and extending third-party high-performance libraries (FlashAttention, cuDNN, Triton, Quack, or similar) into production training stacks

• Experience with CUTLASS, cuDNN APIs, and NCCL internals

• Familiarity with inference optimization techniques and serving frameworks

• Familiarity with diffusion models or multimodal model architectures

• Experience with inference optimization techniques (KV-cache management, speculative decoding, post-training quantization) and serving frameworks (vLLM, TensorRT-LLM)

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.