ML Research Engineer (Agentic Systems)

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 help build infrastructure to power autonomous scientific discovery. This includes scalable agentic frameworks that orchestrate end-to-end research workflows, reproducible experimental pipelines, and the integration layer between frontier models and rich multimodal data. You'll have substantial autonomy to shape foundational decisions on a small, high-impact team. Strong candidates bring ML experience applied to life sciences, strong proficiency with modern Python deep learning libraries and experiment tracking tools, and familiarity with recent developments in AI agents and multimodal world models. You'll have substantial autonomy to shape foundational technical decisions on a small, high-impact team. You'll thrive in this role if you: Are excited about working in a fast-paced, production-focused research lab that often requires switching between many hats Have significant software engineering experience and can move quickly without sacrificing rigor Are able to balance research goals with practical engineering constraints 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, Computational Neuroscience, or a related field
Strong programming skills in Python, with expertise in machine learning frameworks (Pytorch preferred)
Deep understanding of the transformer architecture, multimodal large language models, and agentic frameworks
Experience with TUI / CLI coding agents (e.g. Claude Code, Codex, Gemini-CLI, Qwen Code, etc.)
Experience with agent orchestration frameworks (e.g. Claude Code Agent Teams, Codex Multi-agents; Microsoft Agent Framework, Crew AI Agent Teams, etc.)
Experience in prompt, context, and harness engineering
Knowledge of the latest developments in autonomous AI agent researchers / scientists (e.g. Sakana’s AI Scientist, Edison Scientific’s Kosmos, Analemma’s FARS, Google’s AI co-scientist, Stanford’s Biomni, etc.)
Experience with orchestration platforms and cloud infrastructure (e.g. AWS, GCP, Azure, etc.), including managed ML services
Proficiency with MLOps platforms for experiment tracking and reproducibility (e.g. MLflow, W&B, etc.)
Strong understanding of software engineering best practices, including version control, testing, and documentation

Nice To Haves

Background in cognitive science, neuroscience, or computational neuroscience
Experience or familiarity with building agentic frameworks for scientific discovery, particularly with a focus on results validation and reproducibility
Experience or familiarity with coding agent spec and execution frameworks (e.g. OpenSpec, beads, etc.)
Experience or familiarity with computer use agents (e.g. Claude Computer Use, OAI Operator, Manus, Standard Intelligence’s FDM-1, etc.)
Experience training or fine-tuning large-scale multimodal models
A track record of publications in leading journals and/or machine learning conferences
Contributions to open-source machine learning projects or libraries
Experience scaling machine learning pipelines for consumer-facing applications