About this role
Summary: Build the base intelligence layer for robotics by training large-scale force-aware foundation models. Design pretraining runs, own experiments end-to-end, and research methods to improve sample efficiency and robustness for real-world deployment.
About Enact
The next defining technological breakthrough will be in the physical world. We stand at a historical inflection point: bringing general-purpose intelligence into robotics.
At Enact we rethink the entire robotic tech stack from model architectures and data pipelines, to the actuators and sensors that feed the model. Pushing beyond pure vision and language approaches, we build a proprioceptive nerve system, and the cortex that makes sense of it, to solve dexterity through whole-body force-awareness and deploy robots for thousands of tasks in industry and beyond.
We are shaping a company where brilliant and unconventional people thrive together on solving the hardest challenges of our time. We are truth seekers. We reason from first principles, test our beliefs against reality, and trust that the best ideas can come from anyone. We move fast. We care deeply about our work and colleagues and are driven by passion to change the status quo.
If this resonates, we're excited to hear from you.
In This Role
You will build the base intelligence layer for robotics, training large-scale force-aware foundation models to unlock dextrous manipulation, generalizing across tasks and environments.
- Design and execute large-scale pretraining runs for robot foundation models, defining the architectures, objectives, and training curricula that push AI-driven manipulation beyond pixels to actions.
- Own experiments end-to-end, from data specification through training to real-robot evaluation, to understand scaling laws, data quality effects, and architecture tradeoffs.
- Build datamixes and evaluation sets to train and assess robots on novel, high-precision tasks, across real-world and simulated settings.
- Research methods for improving sample efficiency and robustness, ensuring learned policies generalize reliably from training to real-world deployment.
What We're Looking For
- Deep experience developing large-scale predictive or generative models — including RSSM-style, JEPA, transformer, or diffusion/flow-based architectures.
- Strong probability, statistics, and ML fundamentals, paired with the ability to design rigorous experiments and separate real signal from noise or bugs in experimental data.
- PhD or equivalent research experience with a proven record of research accomplishments in machine learning, computer vision, data-science, robotics and other related fields
- Strong fundamentals in ML computing frameworks like PyTorch or JAX, with familiarity in optimizing for performance and efficiency at scale.
- Motivated to see general-purpose robotic intelligence deployed in the real world.
Bonus Points If You Have
- Prior experience working with real robot hardware
- Led or made significant contributions to multi-node, multi-GPU distributed training efforts.
- Experience designing or scaling data collection, annotation and mixing pipelines
- Track record of open-source contributions or released models/datasets.
- Publications at top robotics or ML venues (CoRL, RSS, ICRA, NeurIPS, ICML, ICLR).
What we offer
- The opportunity to make a real impact working on some of the biggest challenges of our time
- A fast-paced learning environment alongside an outstanding, driven team
- Ownership from day one, with the ability to iterate quickly and see your impact firsthand in an early-stage startup
- Visa sponsorship & relocation benefits to hire the best in the world
- A world-class in-person setup in central Zürich, with excellent prototyping, robotics, and technical infrastructure for hands-on builders
- Competitive compensation and meaningful equity participation