ML Research Scientist (Embodied AI & Reinforcement Learning)

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 AI, neural computation, and embodied intelligence. 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 a Research Scientist to advance the learning algorithms and models that drive our neuro-aligned embodied agents. You will design, train, and evaluate the methods that connect our foundation models to physical systems, working across areas such as world models, action models, reinforcement learning, and learning from demonstration. A central part of your work will be exploring how principles drawn from our neuroscience research can shape better representations, dynamics, and behaviors in embodied agents. Unlike most labs working in this space, Metamorphic approaches embodied intelligence from the joint perspective of machine learning and large-scale neuroscience. This interdisciplinary research paradigm unlocks a new path towards safe AGI, and you will have substantial input into it. You will own end-to-end significant pieces of the research agenda from problem formulation through experimentation, scaling, and evaluation in both simulation and the real world. You will work closely with researchers and engineers across the team, with substantial autonomy over how methods and infrastructure evolve as the work scales.

Requirements

PhD in Machine Learning, Robotics, Computer Science, Computational Neuroscience, or a related field — or equivalent research experience demonstrated through publications and shipped systems
Strong publication record or technical track record in one or more of: reinforcement learning, world models, action models, imitation learning, or embodied AI
Deep working knowledge of modern deep learning, including large-scale training, distributed optimization, and rigorous evaluation methodology
Strong software engineering skills and strong working proficiency in Python and a modern deep learning framework; ability to write training and evaluation infrastructure that other researchers can build on
Hands-on experience training and evaluating policies in simulation
Comfort collaborating closely with engineers and researchers to deploy and evaluate methods in real-world settings, and to iterate across models, environments, and hardware
Scientific rigor in experimental design, ablations, and interpretation of results

Nice To Haves

Experience designing or extending world models, action models, or related architectures for embodied agents
Experience deploying learned policies on real robotic systems, including bimanual or humanoid platforms
Experience with sim-to-real transfer and co-training across simulated and real data
Background in computational or systems neuroscience — neural population analysis, motor control, sensorimotor learning, or biologically-plausible learning rules
Experience using neural data to constrain or evaluate machine learning models
Familiarity with multimodal foundation models and large-scale pretraining
Experience with planning, model-predictive control, or hybrid learning-and-planning approaches
Interest in the relationship between biological and artificial intelligence, and an appetite for cross-disciplinary work

Responsibilities

Design, train, and evaluate methods that connect foundation models to physical systems.
Work across areas such as world models, action models, reinforcement learning, and learning from demonstration.
Explore how principles drawn from neuroscience research can shape better representations, dynamics, and behaviors in embodied agents.
Own end-to-end significant pieces of the research agenda from problem formulation through experimentation, scaling, and evaluation in both simulation and the real world.
Work closely with researchers and engineers across the team, with substantial autonomy over how methods and infrastructure evolve as the work scales.