Member of Technical Staff - 3D Transformers

About the Company

At Machenta, we’re building intelligence that lets any robot learn any new skill as quickly as a human can: instantly, from a single example. We believe that achieving human-level adaptability in the physical world is a key milestone in the future of physical AI, and we’re already seeing glimpses of that future today. As a team, we’re united by this shared vision, and we work closely together in person to bring it to life. On the back of our $40m seed round, we’re now moving into a new office and lab space in the heart of King’s Cross, London. But we’re still right at the beginning here, and we’re looking for the right people to join us all for what lies ahead next. Robotics and machine learning span a diverse technical stack, and wherever you sit within it, everyone at Machenta takes ownership of their part of the company. If that kind of ownership resonates with you, and you’re excited about building together with an ambitious, closely-knit team, then we’d love to speak with you.

About the Role

As a Member of Technical Staff working on 3D machine learning, you’ll refine the model architectures at the heart of instant robot learning. You’ll explore how transformer-based models can build rich 3D representations of the world from multiple sensor inputs, and which architectures, objectives and sources of supervision let them learn the most. You’ll run careful ablations and scaling studies to understand what really drives performance, and turn those insights into better models. It’s a research-driven role at the frontier of machine learning with a practical purpose: every idea you explore is aimed at making our robots more capable. You’ll work closely with everyone at Machenta to see your model improvements come to life on real hardware, and the measure of success is how much better our robots learn and perform.

In This Role You Will:

• Push the frontier of model design for instant robot learning, with a focus on transformer-based architectures and 3D representations.

• Design and run experiments on efficient multimodal sensor fusion, combining inputs such as RGB, depth and proprioception.

• Run ablations and investigate scaling behaviour across model architectures, data and compute.

• Explore new objectives, auxiliary losses and sources of supervision that make learning more data-efficient.

• Turn research insights into models that run efficiently on robot hardware and measurably improve real-world performance.

What We Hope You’ll Bring:

• Deep expertise in modern machine learning, with a strong track record of designing and training transformer-based models.

• Hands-on experience with 3D representations and 3D deep learning, such as point cloud networks or multi-view models.

• A strong understanding of 3D geometry and computer vision fundamentals, including camera models, multi-view geometry and depth estimation.

• 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.

Bonus Points If You Have:

• Experience applying 3D perception or representation learning to robotics, such as manipulation or policy learning.

• Experience with multimodal models, sensor fusion or vision-language-action / world models.

• Experience with self-supervised learning or pretraining on large-scale visual or 3D data.

• Experience optimising models for real-time inference, for example through quantisation, distillation or efficient architectures.

• Publications at leading ML, vision or robotics venues.

• A PhD in a relevant field.

• Experience at an early-stage robotics or AI company.

Join Us

We’re setting out to change how robots learn, and we know it’s an ambitious goal. If you’re excited by the idea of robots picking up new skills the moment they’re shown, and want to help build that future from the ground up, we’d love to hear from you. This role is based in person at our office in London.