London Computer Systems Jobs (NOW HIRING)

We build AI technology for the heavy industries. Our products work reliably in high-volume production environments 24/7, making decisions worth billions of dollars.

Because we work at the intersection of the heavy industries and AI, we solve problems involving manufacturing operations, physics and chemistry, software engineering, AI, and product design. We work to bring AI into the physical world to unlock real value for our customers.

Our vision systems, like ScrapEye, bring real-time computer vision into harsh industrial environments - monitoring scrap metal composition, tracking operations, and automating critical decisions on factory floors. But ScrapEye is just the beginning; we are building a suite of vision-driven AI products that transform physical manufacturing operations.

We operate across two primary hubs in London, United Kingdom and Austin, Texas. For this role, you will be based out of our Austin office (we can accommodate relocation for the right candidate).

We try to work with minimal process and permission, but ample support and collaboration. We rarely have a scheduled meeting, but we all work in the office, discussions occur continuously throughout the day, and no request for help goes unanswered for more than a few minutes. Features get shipped to production every day. Many are designed, developed, and shipped on the customer\'s site, without a central planning process.

As a Computer Vision Engineer, you will own the core visual perception systems powering ScrapEye and our upcoming industrial AI products. You will build, deploy, and iterate on production-grade vision models that operate continuously in real-world mill conditions. This includes:

• Architecting, training, and deploying real-time computer vision models (detection, segmentation, classification, tracking) for industrial video streams.

• Optimizing vision pipelines for edge deployment and high-throughput, low-latency inference in challenging environments (varying lighting, dust, extreme heat).

• Collaborating with forward-deployed engineers to integrate vision outputs directly into operational workflows and AI control loops.

• Building dataset curation, automated labeling, and active learning pipelines to continuously improve model performance in production.

• Exploring and developing new computer vision applications beyond ScrapEye to solve emerging problems across heavy manufacturing.

• Strong fundamentals in deep learning and computer vision (e.g., PyTorch, OpenCV, TensorRT).

• Proven track record of training and deploying vision models into real-world, production environments.

• Strong software engineering skills in Python, with a focus on writing clean, high-performance code.

• Familiarity with edge computing hardware (e.g., NVIDIA Jetson, industrial GPUs) and camera integration protocols.

We are open to a wide range of backgrounds, but some examples we expect to see are:

• Machine learning / computer vision engineers with deployment experience

• Robotics or autonomous systems engineers

• Researchers who have transitioned to building and shipping production software

For all roles, we are looking for people with the following attributes:

• Exceptional ability. Whether in work, school, side projects, or elsewhere, you will have demonstrated exceptional ability. We are open minded about the exact form this takes. Some examples we look for:

  • You have played a key role in an early-stage startup

  • You have won hackathons, math, physics, or computer science competitions

  • You have built impressive vision systems, open-source AI libraries, or deployed ML models into production

• Excellent communication. In speech and writing.

• Conscientiousness. You want to do good work, regardless of oversight.

• You are excited to have freedom to work without too much process and friction, but at the same time, excited to share ideas and work closely with others.

• You're excited to work in the metals industries, and everything that goes with it. That means open to occasional travel to spend time in massive factories, meeting and working with a wide range of people.

• You are comfortable working in uncertain and dynamic environments.

• Prefer mostly work in the office, collocated with your team in Austin, rather than from home (relocation assistance is provided).

• Be open to occasional travel to customer sites for deployments and so that you can understand their operational environment firsthand.