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- Tasks: Join our ML Workflows team to build innovative solutions for complex machine learning challenges.
- Company: G-Research is a top-tier quantitative research firm in London, known for its dynamic culture and talent development.
- Benefits: Enjoy competitive pay, 35 days off, free lunch, and a relaxed dress code.
- Why this job: Work on cutting-edge projects in a collaborative environment that values diversity and innovation.
- Qualifications: Bring engineering experience and a passion for MLOps; finance background not required.
- Other info: Located in the vibrant Soho Place office, opened in 2023.
The predicted salary is between 43200 - 72000 £ per year.
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 engineers to help us build out a mature ML research and deploy pipeline as part of our ML Workflows team. This is an exciting role. You will develop greenfield solutions to meet highly complex ML interdependency requirements. Where off-the-shelf tools fall short, you’ll build custom solutions to define best practice across quantitative research.
Future projects include:
- Implementing best practice feature and model stores
- Properly versioning features, data and models
- Improving inference compute utilisation via model serving
- CI/CD for ML
- Reliably fitting models with complex job dependency graphs
- Robustness in production, including validation and monitoring
Who are we looking for?
You will be an intelligent, pragmatic and capable engineer. You will be comfortable working collaboratively to quickly get to grips with the widely varying requirements across different teams. You will bring industry experience to the table, helping us to apply best practice and drive improvements across our ML operations.
The ideal candidate will have the following skills and experience:
- An appreciation of good architecture and MLOps best practice
- The ability to collaborate with, and influence, technical and non-technical people
- A passion for end-to-end ownership of solutions, from articulation to delivery
- Proven ability to engineer high-quality software
- Effective decision-making, with a focus on the mid to long term
- Value orientated and able to independently prioritise
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
- 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. We want to ensure that applicants receive a recruitment experience that enables them to perform at their best. If you have a disability or special need that requires accommodation please let us know in the relevant section.
Machine Learning Workflow Engineer (Basé à London) employer: Golden Bees
Contact Detail:
Golden Bees Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Workflow Engineer (Basé à London)
✨Tip Number 1
Familiarise yourself with MLOps best practices and architecture. Understanding the principles behind effective machine learning workflows will help you stand out during discussions and interviews.
✨Tip Number 2
Showcase your collaborative skills by preparing examples of how you've worked with both technical and non-technical teams in the past. This will demonstrate your ability to bridge gaps and influence diverse groups.
✨Tip Number 3
Be ready to discuss your experience with custom solutions and how you've tackled complex ML interdependencies. Highlighting specific projects where you've implemented innovative solutions can set you apart.
✨Tip Number 4
Research G-Research's culture and values, especially their commitment to diversity and inclusion. Being able to articulate how your personal values align with theirs can make a positive impression.
We think you need these skills to ace Machine Learning Workflow Engineer (Basé à London)
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and requirements of the Machine Learning Workflow Engineer position. Familiarise yourself with key terms like MLOps, CI/CD, and model serving to demonstrate your knowledge in your application.
Tailor Your CV: Customise your CV to highlight relevant experience and skills that align with the job description. Emphasise your engineering background, software development skills, and any experience with machine learning workflows or similar projects.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for machine learning and your ability to work collaboratively. Mention specific projects or experiences that demonstrate your problem-solving skills and your understanding of best practices in ML operations.
Highlight Soft Skills: In addition to technical skills, emphasise your ability to communicate effectively with both technical and non-technical stakeholders. This is crucial for the role, so provide examples of how you've successfully collaborated in past projects.
How to prepare for a job interview at Golden Bees
✨Understand MLOps Best Practices
Familiarise yourself with MLOps principles and best practices. Be prepared to discuss how you would implement these in a real-world scenario, especially in relation to feature and model stores.
✨Showcase Your Collaboration Skills
Since the role requires working with both technical and non-technical teams, be ready to provide examples of how you've successfully collaborated in the past. Highlight your ability to communicate complex ideas clearly.
✨Demonstrate Problem-Solving Abilities
Prepare to discuss specific challenges you've faced in previous projects and how you approached solving them. This will showcase your pragmatic thinking and decision-making skills.
✨Express Passion for End-to-End Ownership
Convey your enthusiasm for taking ownership of projects from conception to delivery. Share experiences where you have driven a project forward and the impact it had on your team or organisation.
