HPC/AI Research Engineer (UK)

Are you an early career researcher with some exposure to AI & HPC, looking to develop your skills in the context of a funded research project? If so, read on!

If you are interested in working for a long-successful company that feels like a start-up, NAG could be the company for you! Founded over 50 years ago, we are now transforming and growing, and looking for an energetic individual to join our Research Lab. The successful applicant will be contributing to the DECICE project – an EU-funded collaboration project in which an impressive consortium of partners is building cloud-to-edge AI infrastructure. You will join our existing R&D team but also be part of the DECICE consortium and so will have lots of opportunity to grow your knowledge in a real research engineering setting.

Increasing your knowledge will be part of your make up, you’ll be passionate about developing your HPC/AI skills and will be a valuable part of our technical knowledge base. 

The successful applicant will:

  • Develop significant parts of the cloud-to-edge framework, including optimization of hybrid AI models, implementing a cost model to assess the performance and cost of running models, and software engineering of these components.
  • Work directly with members of the DECICE consortium to understand how NAG’s components can be integrated with broader framework
  • Travel within Europe to collaborate with DECICE colleagues outside of NAG.

NAG employees work flexibly with location and work hours suited to the employee and their team. If you prefer working around colleagues, we have an office in central Oxford. Otherwise, you can be one of our many staff that prefer working from their own home. We have mastered hybrid-remote working and all NAG teams operate in this fashion with someone’s personal preference not an impediment.

About you

The desired candidate will have:

  • A BSc or MSc (or equivalent) in an applied science;
  • Experience programming with at least one of Fortran, C, C++ or Python;
  • Some exposure to the use of deep neural nets via machine learning frameworks and the ability to modify the implementation for improved accuracy or performance.
  • Experience improving the performance of programs, either through mathematical techniques or through technical performance tuning

Candidates that can demonstrate one or more of the following be very welcomed:

  • Knowledge of schedulers in high performance computing environments, e.g. SLURM, PBS, etc.
  • Exposure to cost models in the high performance computing or machine learning environments
  • Knowledge of machine learning algorithms “under-the-hood” and the fundamental kernels that can affect performance of such systems

Culture and Benefits   

We provide a host of benefits including a competitive salary (dependent on your experience),  a generous pension contribution of 21.6%, private health care (we cover the tax on your premium too!), life insurance, and 30 days annual leave (with the option to buy or sell up to 5 days). Our structure means that we get to know each employee as an individual and the contribution their work brings to our success as an organisation, allowing us to recognise and reward our rising stars.


Please provide a CV to hradmin@nag.co.uk

We believe that a diverse workforce helps us develop innovative products and services.  To enable this, we operate a blind recruitment process and all information with the potential to introduce conscious or unconscious bias will be redacted during the shortlisting process.

About NAG

NAG provides industry-leading numerical software and technical services to banking and finance, energy, engineering, and market research, as well as academic and government institutions. World renowned for the NAG® Library - the most rigorous and robust collection of numerical algorithms available - NAG also offers Automatic Differentiation, Machine Learning, and Mathematical Optimization products, as well as world-class technical consultancy across HPC and Cloud HPC, code porting and optimization, and other areas of numerical computing. Founded more than 50 years ago from a multi-university venture, NAG is headquartered in Oxford, UK with offices in the UK, US, EU and Asia.