Robotics AI Research Scientist — Foundation Models

Arbeitnow; Zürich; On-site; CHF 140,000 - 190,000; Full time

Benefits offered by this job

Visa sponsorship & relocation

Central Zürich in-person setup

Equity participation

Competitive compensation

Ownership from day one

Job summary

Enact is building the base intelligence layer for robotics, training large-scale force-aware foundation models to unlock dexterous manipulation and generalize across tasks and environments.

You will design and run large-scale pretraining, own experiments end-to-end from data specification through training to real-robot evaluation, and build datamixes and evaluation sets for novel tasks in real-world and simulated settings. This is a hands-on, fast-paced opportunity in Zürich.

Qualifications

• PhD or equivalent research experience with a focus on ML, CV, robotics, or data science.
• Strong fundamentals in probability, statistics and ML, with ability to design rigorous experiments.
• Experience designing large-scale predictive or generative models and evaluating them rigorously.
• Hands-on experience with ML computing frameworks like PyTorch or JAX and optimizing at scale.

Responsibilities

• Design and execute large-scale pretraining runs for robot foundation models, defining architectures, objectives and training curricula.
• Own experiments end-to-end—from data specification through training to real-robot evaluation.
• Build datamixes and evaluation sets to train and assess robots on novel tasks across real-world and simulated settings.
• Research methods to improve sample efficiency and robustness, ensuring generalization to real-world deployment.

Skills

Large-scale ML models

Experiment design

Research methodology

Transformer architectures

Proficiency in ML fundamentals

Data-driven reasoning

Education

PhD or equivalent research experience

Tools

PyTorch

JAX

Job description

Enact is building the base intelligence layer for robotics, training large-scale force-aware foundation models to unlock dexterous manipulation and generalize across tasks and environments.

You will design and run large-scale pretraining, own experiments end-to-end from data specification through training to real-robot evaluation, and build datamixes and evaluation sets for novel tasks in real-world and simulated settings. This is a hands-on, fast-paced opportunity in Zürich.