G10X
Senior Machine Learning Engineer

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Job Description
Significant experience designing and deploying machine learning systems in production environments.
Strong software engineering skills in Python, with experience building maintainable, tested, and production-quality code.
Strong experience with large-scale data processing using technologies such as PySpark and Databricks.
Experience designing and building ML pipelines across the full lifecycle, from data preparation and model development through to deployment and monitoring.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Experience developing deep learning models using frameworks such as PyTorch, TensorFlow, or similar.
Experience with multimodal machine learning, representation learning, or embedding models, combining data sources such as images, text, structured metadata, or behavioural signals.
Strong understanding of modern deep learning architectures, particularly Transformers, foundation models, multimodal learning, and representation learning techniques.


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Your very own career expert that helps elevate your application to the next level.
Experience working with distributed computing, large datasets, and scalable model training or inference systems.
Familiarity with cloud platforms and modern MLOps practices.
Strong communication skills and the ability to collaborate effectively with scientists and engineers.
A pragmatic mindset, balancing technical excellence with delivering business value.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
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