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Senior Machine Learning Engineer

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Machine Learning Engineer – London
I’m working with a highly technical organisation in London that is looking for a Machine Learning Engineer to join a team focused on the systems and infrastructure behind large scale ML.
This is less about building models all day and more about making sure the technology around them can handle the scale, performance and complexity required.
What you’ll be working on:
- Improving the performance and scalability of ML workloads across training, inference and distributed compute
- Profiling systems, finding bottlenecks and understanding how compute, networking and storage impact performance
- Working with Python, PyTorch, NumPy and other ML tooling in GPU heavy and high performance environments
- Testing new frameworks, hardware and approaches, from quick prototypes through to more robust engineering solutions
- Solving technical problems that need strong engineering judgement rather than an off-the-shelf answer
- Working closely with engineers and researchers across ML, infrastructure and compute
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.
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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.
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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.
What makes the opportunity interesting:
- You’ll be working on ML problems at a scale and level of complexity most companies don’t operate at
- The role gives you exposure to large scale distributed systems, serious compute and performance sensitive workloads
- You’ll have the freedom to properly investigate new technology rather than just work within an existing stack
- It suits someone who is strong in Python and ML engineering, with experience in areas like distributed training, inference, profiling, optimisation or HPC
- A strong Computer Science, Machine Learning, Maths or related background is useful, although equivalent commercial experience is equally relevant
- You can stay deeply technical and continue solving difficult engineering problems without needing to move into management


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This would suit someone who enjoys the engineering side of machine learning and wants to work somewhere scale, performance and technical complexity genuinely matter.
“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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