Research Scientist: Post-Training

ph3About Enact /h3 pThe next defining technological breakthrough will be in the physical world. We stand at a historical inflection point: bringing general-purpose intelligence into robotics. /p pAt 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. /p pWe 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. /p h3In This Role /h3 pYou will build the base intelligence layer for robotics, training large-scale force-aware foundation models to unlock dextrous manipulation, generalizing across tasks and environments. /p ul liDesign and execute large-scale pre-training runs for robot foundation models, defining the architectures, objectives, and training curricula that push AI-driven manipulation beyond pixels to actions. /li liOwn experiments end-to-end, from data specification through training to real-robot evaluation, to understand scaling laws, data quality effects, and architecture tradeoffs. /li liBuild data mixes and evaluation sets to train and assess robots on novel, high-precision tasks, across real-world and simulated settings. /li liResearch methods for improving sample efficiency and robustness, ensuring learned policies generalize reliably from training to real-world deployment. /li /ul h3What We're Looking For /h3 ul liDeep experience developing large-scale predictive or generative models - including RSSM-style, JEPA, transformer, or diffusion/flow-based architectures. /li liStrong probability, statistics, and ML fundamentals, paired with the ability to design rigorous experiments and separate real signal from noise or bugs in experimental data. /li liPhD or equivalent research experience with a proven record of research accomplishments in machine learning, computer vision, data-science, robotics and other related fields /li liStrong fundamentals in ML computing frameworks like PyTorch or JAX, with familiarity in optimizing for performance and efficiency at scale. /li liMotivated to see general-purpose robotic intelligence deployed in the real world. /li /ul h3Bonus Points If You Have /h3 ul liPrior experience working with real robot hardware /li liLed or made significant contributions to multi-node, multi-GPU distributed training efforts. /li liExperience designing or scaling data collection, annotation and mixing pipelines /li liTrack record of open-source contributions or released models/datasets. /li liPublications at top robotics or ML venues (CoRL, RSS, ICRA, NeurIPS, ICML, ICLR). /li /ul h3What We Offer /h3 ul liThe opportunity to make a real impact working on some of the biggest challenges of our time /li liA fast-paced learning environment alongside an outstanding, driven team /li liOwnership from day one, with the ability to iterate quickly and see your impact firsthand in an early-stage startup /li liVisa sponsorship relocation benefits to hire the best in the world /li liA world-class in-person setup in central Zürich, with excellent prototyping, robotics, and technical infrastructure for hands-on builders /li liCompetitive compensation and meaningful equity participation /li /ul /p #J-18808-Ljbffr