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Machenta Ltd in London is seeking a Member of Technical Staff to advance robot learning, enabling robots to acquire new skills from a single demonstration and to design experiments on representation learning for in-context imitation learning.
You will work closely with the team to scale training, evaluate policies on real robots, and explore data-driven strategies to improve model performance, contributing to a fast-moving, research-driven environment.
We've been building Machenta for less than a year, and so we're still right at the beginning here. We're a very close team, sharing the journey together, working in person in London, and collaborating throughout. This has been great fun! And as we bring in the next few team members, we're now thinking carefully about what roles we need, and who we should be speaking with. Everybody joining will be Machenta's expert in their part of the team, with ownership and responsibility for shaping where we go next and how we get there. We pay well, and we'll be giving you meaningful equity, private health insurance, and lots of free food.
As a Member of Technical Staff working on robot learning, you'll be working on the core models that let our robots pick up new skills from just a single demonstration. You'll design and run experiments on representation learning for in-context imitation learning, explore how the quality, quantity and diversity of data shape what our models can do, and help scale training and evaluation so we can test ideas quickly and at scale. It's a research-driven but highly practical role: you'll take ideas from first experiments to policies running on real robots, working closely with everyone at Machenta. If you're excited by the moment a robot does something new after seeing it only once, you'll find this work very rewarding.
Push the frontier of instant robot learning, enabling robots to acquire new skills from minimal demonstrations.
Design and run experiments on representation learning for in-context imitation learning.
Lead investigations into how data quality, quantity and diversity affect model performance, and turn findings into data generation strategies.
Explore how reinforcement learning can improve our models, both through policy training and targeted data generation.
Optimise and scale model training and evaluation pipelines across large datasets and compute.
Deploy and evaluate policies on real robots to drive model improvements.
Hands-on experience training and evaluating learning-based policies for robotics.
A deep understanding of modern deep learning, including transformers, sequence models and generative policy classes such as diffusion or flow-based models.
Strong Python and PyTorch skills, and experience writing clean, reliable code.
Experience running large-scale training, including distributed training and efficient data pipelines.
A rigorous, experiment-driven mindset: careful baselines, ablations and clear conclusions.
Clear communication of your ideas and findings to the team.
Experience with in-context learning, meta-learning or few-shot adaptation.
Experience deploying learned policies on real robot hardware.
Experience building or curating large robotics datasets, or working with large vision-language-action models.
Publications at leading ML or robotics venues.
A PhD in a relevant field.
Experience at an early-stage robotics or AI company.
We're setting off to change how robots learn, and how people interact with them. If you're excited about joining in on the adventure with us all, and about working in a close-knit, highly ambitious, highly passionate team in the heart of King's Cross London, we would love to hear from you!