Machine Learning Engineer

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

G-Research is Europe’s leading quantitative finance research firm. We hire the brightest minds in the world to tackle some of the biggest questions in finance. We pair this expertise with machine learning, big data, and some of the most advanced technology available to predict movements in financial markets.

The role

We are looking for exceptional software engineers to work on cutting-edge machine learning algorithms and hardware accelerators.

G-Research’s Machine Learning Engineers have the opportunity to join one of three teams:

• Machine Learning Engineering
• The Technology Innovation Group
• Open-Source Software

Machine Learning Engineering (embedded within Quantitative Research)

As a member of the Machine Learning Engineering team, you will work alongside our quantitative researchers and engage in a mix of individual and collaborative projects, which enable researchers to tackle some of their toughest problems.

In this role, you will use a combination of off-the-shelf tools and custom solutions written from scratch, to drive the latest advances in quantitative research.

Past projects have included:

• Implementing ideas from a recently published research paper
• Writing custom libraries for efficiently training on terabytes of data
• Reducing model training times by hand optimising machine learning operations
• Profiling research code to identify performance bottlenecks
• Evaluating the latest hardware and software in the machine learning eco-system

Technology Innovation Group (working on new machine learning accelerators)

As a member of the Technology Innovation Group, you will support our Research and Engineering teams as they seek to take advantage of next generation machine learning hardware accelerators.

You will be working as part of a small, diverse, interdisciplinary, fast-paced team to help drive adoption of new machine learning hardware and software technologies.

We want experienced, confident ML practitioners who have the ability to understand new ML tools quickly, techniques and technology, and test them to their breaking point, before advising on where (or where not) they may be useful. You will be comfortable trying out brand new ML libraries or accelerators one week, and presenting on their pros and cons the next.

Your responsibilities will include:

• Building proofs of concept with new data science and machine learning technologies to demonstrate how they could add value at any stage in the ML lifecycle
• Designing representative benchmarks to evaluate new technologies and deep-diving on their performance characteristics
• Liaising with vendors, providing constructive feedback on their products and roadmaps
• Collaborating with other G-Research teams on initiatives that require significant experimentation, such as tuning the performance of distributed machine learning on our infrastructure
• Creating working examples of machine learning models to run on alternative hardware accelerators to NVIDIA A100
• Adapting existing models to make use of different hardware accelerator architectures to improve model training speed
• Identifying use cases and matching them to different hardware accelerators
• Helping to identify gaps in new vendor technologies and tool chains
• Acting as a Developer Relations (DevRel) engineer and technology advocate to help drive adoption in a way that improves developer experience and productivity
• Creating papers and talks on how new machine learning accelerators can be used within G-Research
• Attending conferences to stay up-to-date on the latest trends and new technologies

Open Source Software (contributing to open-source machine learning frameworks)

As a member of the Open-Source Software team, you will work with other G-Research teams to understand how we use machine learning and how the open-source ecosystem could be developed.

You will work as part of a globally distributed open source team, and with the open source community, to get involved in future roadmaps, influence engineering directions and make contributions to open-source machine learning frameworks and libraries.

Your responsibilities will include:

• Contributing code to open-source initiatives in the machine learning ecosystem
• Working with the open-source community to understand and contribute to the roadmap for machine learning frameworks and libraries
• Working with engineers in G-Research on how existing and future open-source features could be used
• Working with G-Research on how open-source initiatives can be further extended to better support our use cases or better exploit architectural differences in new hardware accelerators
• Building proofs of concept with new data science and machine learning technologies that demonstrate how they could add value at any stage in the ML lifecycle
• Designing representative benchmarks to evaluate new technologies and deep-diving on their performance characteristics
• Liaising with vendors, providing constructive feedback on their products and roadmaps
• Collaborating with other G-Research teams on initiatives that require significant experimentation, such as tuning the performance of distributed machine learning on our infrastructure

Who are we looking for?

Candidates will be comfortable working both independently and in small teams on a variety of software engineering challenges, with a particular focus on machine learning and scientific computing.

The ideal candidate will have:

• Demonstrable ability to work with the scientific Python eco-system or other scientific computing languages, for example Julia or Matlab
• Experience with machine learning either from prior employment, coursework, or personal projects
• Ability to write one-off scripts and unit tested libraries as well as being able to decide when each is appropriate
• An interest in keeping up with new developments in the field, including papers and new technologies
• Capability to communicate new and complex knowledge from project work back to the team

Finance experience is not necessary for this role and candidates from non-financial backgrounds are encouraged to apply.

Why should you apply?

• Highly competitive compensation plus annual discretionary bonus
• Lunch provided (via Just Eat for Business) and dedicated barista bar
• 30 days’ annual leave
• 9% company pension contributions
• Informal dress code and excellent work/life balance
• Comprehensive healthcare and life assurance
• Cycle-to-work scheme
• Monthly company events

G-Research is committed to cultivating and preserving an inclusive work environment. We are an ideas-driven business and we place great value on diversity of experience and opinions.