Machine Learning Workflow Engineer

G-Research
London
1 month ago
Create job alert

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 (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

.
#J-18808-Ljbffr

Related Jobs

View all jobs

Machine Learning Workflow Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Software Engineer - (Machine Learning Engineer) - Hybrid

Data Engineer (AWS & Airflow)

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.