ML Research Engineer (Computer Vision)

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 seeking Research Engineers to join our growing AI research team. You will build and scale state-of-the-art foundation models that power Metamorphic's next-generation systems. This is a high-impact, technically deep role at the frontier of ML research and engineering, with a focus on developing, scaling, and productionizing computer vision deep learning models for real-world multimodal data pipelines. A strong background in computer vision is essential. 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 the ability to move from research code to production-quality systems

• Are a pragmatic problem solver with attention to scalability and reliability

• Are comfortable operating in fast-moving, research-driven environments

• 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

$175,000 - $250,000 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, Electrical Engineering, Physics, or another quantitative field

• Proficiency in Python with strong software engineering practices (testing, modular design, reproducibility, version control)

• Practical experience with deep learning frameworks such as PyTorch

• Practical experience with workflow orchestrators such as Ray, Dagster, Airflow, DVC or similar and experience in implementing and maintaining complex machine learning pipelines

• Practical experience in collecting, maintaining, and working with large multimodal datasets

• Strong experience in deep learning solutions to computer vision and image processing tasks (depth estimation, object detection, segmentation, etc.)

• Hands-on experience in optimizing inference efficiency, memory usage, and throughput of computer vision models.

Nice to Have

• Experience conducting long-term development and/or research projects in academia or industry

• Familiarity with large-scale data processing pipelines

• Contributions to open-source ML projects

• Publications at top-tier conferences such as CVPR, ICCV, ECCV, ICLR, ICML, NeurIPS

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.