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- Tasks: Work on cutting-edge machine learning problems alongside quantitative researchers.
- Company: G-Research is a leading tech firm in finance, fostering talent in a dynamic culture.
- Benefits: Enjoy competitive pay, 35 days leave, free lunch, and a relaxed dress code.
- Why this job: Tackle tough research questions with top-notch tools in a collaborative environment.
- Qualifications: Post-graduate degree or commercial experience in machine learning; strong Python skills required.
- Other info: No finance background needed; all backgrounds encouraged to apply.
The predicted salary is between 48000 - 84000 £ per year.
We tackle the most complex problems in quantitative finance, by bringing scientific clarity to financial complexity.
From our London HQ, we unite world-class researchers and engineers in an environment that values deep exploration and methodical execution – because the best ideas take time to evolve. Together we’re building a world-class platform to amplify our teams’ most powerful ideas.
As part of our engineering team, you’ll shape the platforms and tools that drive high-impact research – designing systems that scale, accelerate discovery and support innovation across the firm.
Take the next step in your career.
The role
We are looking for exceptional machine learning engineers to work alongside our quantitative researchers on cutting-edge machine learning problems.
As a member of the Core Technical Machine Learning team, you will be engaged in a mixture of individual and collaborative work to tackle some of the toughest research questions.
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 petabytes of data
- Reducing model training times by hand optimising machine learning operations
- Profiling custom ML architectures to identify performance bottlenecks
- Evaluating the latest hardware and software in the machine learning ecosystem
Who are we looking for?
Candidates will be comfortable working both independently and in small teams on a variety of engineering challenges, with a particular focus on machine learning and scientific computing.
The ideal candidate will have the following skills and experience:
- Either a post-graduate degree in machine learning or a related discipline, or commercial experience working on machine learning models at scale. We will also consider exceptional candidates with a proven record of success in online data science competitions, such as Kaggle
- Strong object-oriented programming skills and experience working with Python, PyTorch and NumPy are desirable
- Experience in one or more advanced optimisation methods, modern ML techniques, HPC, profiling, model inference; you don’t need to have all of the above
- Excellent ML reasoning and communication skills are crucial: off-the-shelf methods don’t always work on our data so you will need to understand how to develop your own models in a collaborative environment working in a team with complementary skills
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 (viaJust Eat for Business) and dedicated barista bar
- 35 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
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Machine Learning Engineer employer: G-Research
Contact Detail:
G-Research Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with the latest machine learning techniques and tools, especially those mentioned in the job description like Python, PyTorch, and NumPy. Being well-versed in these technologies will not only boost your confidence but also demonstrate your commitment to the role.
✨Tip Number 2
Engage with the machine learning community by participating in online forums or competitions such as Kaggle. This will help you build a strong portfolio and network with professionals in the field, which can be beneficial when applying for roles like this one.
✨Tip Number 3
Prepare to discuss your past projects in detail, particularly those that involved custom solutions or optimising machine learning operations. Being able to articulate your thought process and problem-solving skills will set you apart during interviews.
✨Tip Number 4
Research G-Research and their recent projects or publications. Understanding their work and how you can contribute will show your genuine interest in the company and the role, making you a more attractive candidate.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning and programming. Emphasise any projects or roles where you've used Python, PyTorch, or NumPy, as well as any advanced optimisation methods you've worked with.
Craft a Strong Cover Letter: In your cover letter, express your enthusiasm for tackling complex machine learning problems. Mention specific projects or experiences that align with the role at G-Research, showcasing your ability to work both independently and collaboratively.
Showcase Your Skills: If you have participated in data science competitions like Kaggle, be sure to include this in your application. Highlight any achievements or unique approaches you took in these competitions to demonstrate your problem-solving skills.
Prepare for Technical Questions: While this is not part of the written application, it's wise to prepare for potential technical questions related to machine learning concepts. Brush up on your understanding of model training, profiling, and optimisation techniques, as these may come up in interviews.
How to prepare for a job interview at G-Research
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python, PyTorch, and NumPy. Bring examples of projects where you've implemented machine learning models, especially those that demonstrate your ability to work with large datasets or custom solutions.
✨Understand the Company’s Focus
Research G-Research and their approach to quantitative research. Familiarise yourself with recent advancements in machine learning and be ready to discuss how they can be applied to finance, even if you don't have a financial background.
✨Prepare for Problem-Solving Questions
Expect to tackle technical challenges during the interview. Practice explaining your thought process when solving complex problems, particularly those related to optimising machine learning operations or profiling ML architectures.
✨Demonstrate Collaboration Skills
Since the role involves working in small teams, be ready to share examples of how you've successfully collaborated with others on projects. Highlight your communication skills and your ability to develop models in a team environment.
