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- Tasks: Work on cutting-edge machine learning problems alongside quantitative researchers.
- Company: G-Research is a top-tier quantitative research and tech firm in London.
- Benefits: Enjoy competitive pay, 35 days leave, free lunch, and a relaxed dress code.
- Why this job: Tackle big finance questions with innovative tech in a dynamic, supportive culture.
- 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.
Join to apply for the Machine Learning Engineer role at G-Research
Join to apply for the Machine Learning Engineer role at G-Research
Do you want to tackle the biggest questions in finance with near infinite compute power at your fingertips?
G-Research is a leading quantitative research and technology firm, with offices in London and Dallas. We are proud to employ some of the best people in their field and to nurture their talent in a dynamic, flexible and highly stimulating culture where world-beating ideas are cultivated and rewarded.
This is a role based in our new Soho Place office opened in 2023 – in the heart of Central London and home to our Research Lab.
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 dont need to have all of the above
- Excellent ML reasoning and communication skills are crucial: off-the-shelf methods dont 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 (via Just 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
- Monthly company events
Location: London
Seniority level
- Seniority levelNot Applicable
Employment type
- Employment typeFull-time
Job function
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Machine Learning Engineer (London) employer: G-Research
Contact Detail:
G-Research Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (London)
✨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 stay updated on current trends and showcase your skills, making you a more attractive candidate.
✨Tip Number 3
Network with professionals in the finance and tech sectors, particularly those who work in quantitative research. Attend relevant meetups or webinars to gain insights and potentially get referrals that could enhance your application.
✨Tip Number 4
Prepare to discuss your past projects in detail, especially those involving custom solutions or optimisations. Be ready to explain your thought process and the impact of your work, as this will highlight your problem-solving abilities and technical expertise.
We think you need these skills to ace Machine Learning Engineer (London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, programming skills (especially Python, PyTorch, and NumPy), and any projects that demonstrate your ability to tackle complex problems. Use specific examples to showcase your achievements.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention why you are interested in working at G-Research and how your background aligns with their needs. Highlight any unique experiences or skills that set you apart from other candidates.
Showcase Your Projects: If you have worked on machine learning projects, especially those involving custom solutions or optimisations, be sure to include them in your application. Provide links to your GitHub or any relevant portfolios to give them a clear view of your capabilities.
Prepare for Technical Questions: Be ready to discuss your technical skills and experiences in detail. Think about how you would approach common machine learning challenges and be prepared to explain your reasoning and problem-solving process clearly.
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 past projects where you implemented machine learning models, especially those that required custom solutions or optimisations.
✨Demonstrate Problem-Solving Abilities
Expect to tackle some technical challenges during the interview. Practice explaining your thought process when approaching complex problems, particularly in machine learning and scientific computing.
✨Communicate Effectively
Strong communication skills are crucial for this role. Be ready to explain your ideas clearly and concisely, especially when discussing how you would develop models collaboratively with a team.
✨Research the Company Culture
G-Research values a dynamic and flexible work environment. Familiarise yourself with their culture and be prepared to discuss how you can contribute to their innovative atmosphere, even if you come from a non-financial background.
