Senior Simulation Engineer

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 Role

We are looking for a highly skilled and experienced Senior Simulation & Modelling Engineer to join our multidisciplinary delivery team. 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. In this role you will be both expanding our existing physics engines and developing new frameworks for future markets. 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. The code frameworks you will work on include multi-physics simulators and specialised engineering process models. Ideally, you will have strong software engineering skills and experience working in a research environment. You will be able to work with a high degree of autonomy, with the ability to engage with the mathematics and physics literature,

seeking out domain expertise as required. 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.

Key Responsibilities:

• Implement mathematical models of real world physical systems. 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).
• Work with application engineers to capture relevant physics underpinning a customers application, developing requirements for the physics engines.
• 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.

Required Qualifications and Experience:

• University degree in a relevant area of engineering/science (physics, maths, computer science) or an equivalent qualification. An advanced degree may be an advantage but is not required.
• 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 understanding of the numerical techniques related to physics codes, e.g. differential equation solvers, numerical integration schemes, statistical sampling, finite element methods
• 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).
• Must be able to autonomously research and learn about new systems/processes. Be able to make and justify approximations relevant to the simulation fidelity required.
• 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.
• Excellent communication skills.

Behaviours and personal characteristics:

• Passionate about writing robust, efficient code that is well thought out and tested.
• Fast learner, comfortable picking up new technologies and techniques.
• Product focused, good prioritisation skills.
• T-shaped person, deep expertise in a few areas but able to be a generalist when needed.
• Team player.
• Enjoys teaching junior staff about how to improve their modelling and code.
• Strong organisational skills.

Benefits:

• 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