Senior Engineer
3 weeks ago
The Company Luffy AI is an exciting high-tech startup developing adaptive neural networks for industrial control. Luffy specialises in adaptive neural networks that can be trained in simulation on digital twins and successfully transferred into real world systems. Our networks use neural plasticity to learn the dynamics of the equipment they are placed in and continue to adapt long after training. These innovations allow us to overcome the problems of using Machine Learning in control system applications.
Our transformational AI technology allows manufacturers to improve productivity and save energy. It also allows robotics companies to extend the operating envelope of their machines, making more robust and capable robotics. This revolutionary control technology is a key enabler of Industry 4.0, with huge potential in foundation industries such as metals, glass and cement manufacturing, as well as robotic systems such as industrial arms, drones and sub-sea UAVs.
The team is responsible for building our suite of AI and physics model products for control and optimisation of industrial equipment. We are looking for someone with deep practical experience in physical modelling who is looking to help us solve real world problems with artificial intelligence.
Our customers span a range of robotics and industrial process companies. When training AI networks for these applications, we require digital twin models that expose the networks to a representative approximation of the system dynamics and phenomena encountered. AI controllers are trained on these digital twins using Reinforcement Learning techniques, and in some instances, the physics model is also a product offering. These digital twin models are built on in-house physics engines, which are configured for the application under development. Working closely with AI application engineers, you will design physics APIs to support the development of AI training environments.
This dynamic role will require you to have a strong interest in modelling real world systems and software engineering, with broader interests such as AI, high performance computing, and mechanical or electrical systems. Ideally, you will have strong software engineering skills and experience working in a research environment. You care deeply about software standards and enjoy working on challenging research problems, sometimes requiring experimentation. Examples of excellent practical experience include developing against/for game physics engines (e.g. React, Bullet, MuJoCo), industrial/chemical process models, implementing numerical physics solvers, and algorithm design and optimisation.
Develop our core physics engines and reference models (e.g. aerodynamic, mechanical, industrial processes), with a focus on computational performance.
Assist the AI team with digital twin development. Ensure that game engines are fit for their intended uses (numerical stability, performance, accuracy).
Maintain good code quality standards across our physics code bases.
Develop visualisation tools for digital twins and physics engines, alongside other developers.
Investigate problem areas to advance knowledge, evaluate new approximations and numerical techniques for validity and performance.
University degree in a relevant area of engineering/science (physics, maths, computer science) or an equivalent qualification. 5+ years of relevant experience in one of games physics programming, digital twin development, chemical process modelling, or other physical simulations balancing performance and fidelity.
~ Skilled in software architecture with a good understanding of algorithm design, algorithmic complexity, managing computational resources, design patterns and API design.
~ Strong mathematical skills, including vector calculus, linear algebra, 3D geometry.
~ Strong programming skills in Python, experience with C++ desirable.
~ Ability to produce visualisation tools that support simulation models (graphing and 3D visualisations, Python/JS).
~ Good understanding of code profiling, performance and numerical optimisation techniques.
~ Familiarity with testing frameworks and continuous integration.
~ A desire to help other people solve their problems.
~ Passionate about writing robust, efficient code that is well thought out and tested.
Fast learner, comfortable picking up new technologies and techniques.
Full time salary of £50k - £70k, depending on experience and capability assessment during the interview process.
~ EMI share options scheme.
~25 days annual leave, plus bank holidays
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