Zettafleet
Senior Forward-Deployed Data Engineer

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Location: London (On-site; Liverpool Street)
Employment Type: Full-time and Permanent
Remuneration: £120–145k Base Salary + Discretionary Bonus + Equity
About Zettafleet
Zettafleet is an end-to-end platform for businesses and organisations to train their own LLM on their proprietary data. We can use non-conventional AI hardware and automatically source and combine GPUs (and other types of AI accelerators) from multiple cloud providers, enabling users to optimise for cost, duration or geographic location of the training.
The founding team consists of Oxford and Cambridge graduates and former engineers at Google, Meta, Microsoft and Amazon. We are backed by prominent investors from the US and the UK, including institutional VC funds and C-level executives of global technology companies.
The Role
We are looking for an experienced, high-impact Senior Forward-Deployed Data Engineer to bridge the gap between complex enterprise data environments and advanced AI models. In this high-visibility, customer-facing technical role, you will embed with client teams to turn their raw data into clean Parquet files for training of domain-adapted LLMs and embedding models. You will act as both a senior data architect and AI consultant, advising clients on the complete data lifecycle – from dataset creation and synthetic data generation to identifying the right data retrieval and LLM training strategies.
In this role, you will:
- Partner directly with customer engineering leadership and product teams to understand their business roadmap, technical requirements and existing data infrastructure.
- Architect and build production-grade data pipelines to transform raw enterprise data into training-ready formats.
- Design and implement data cleaning, filtering, deduplication and synthetic data generation strategies tailored for Continued Pre-Training (CPT), Supervised Fine-Tuning (SFT), Direct Preference Optimisation (DPO) and Reinforcement Learning (RL) workflows.
- Continuously evaluate model input and output quality, iteratively refining dataset preparation and training strategies to optimise performance.
- Work with Zettafleet’s in-house engineering, research and data teams to solve non-trivial data challenges and edge cases.
- Capture insights, friction points and feature requests to directly influence Zettafleet’s core product roadmap.
- Occasionally travel to the customer’s place of work, whether it’s in the UK or overseas.
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 We Are Looking For
- Collaboration and communication: Genuinely exceptional verbal and written communication skills, capable of translating complex Data Science concepts into actionable strategies for executive and technical audiences alike.
- Data engineering: 3+ years of experience architecting and building data pipelines using Python. Overall, a minimum of 5 years of professional work experience in software engineering or computer science, which involved programming.
- Applied knowledge of LLMs: Solid understanding and practical experience with the modern LLM lifecycle, including data tokenisation, synthetic data generation, pre-training, post-training, vector databases and RAG.
- Problem solving: Strong analytical problem-solving skills and attention to detail.
- Strong ownership mindset: Ability to take full responsibility for outcomes, not just tasks, and great care about the quality of deliverables.


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We would like to acknowledge that almost no candidate checks every box – and that is perfectly fine. If you are passionate about solving complex challenges and open to learning new technologies, we would love to hear from you.
Nice to Have
- Proven track record in a customer-facing technical role (e.g., forward-deployed engineering, solutions architecture or technical consulting) with strong stakeholder management abilities.
- Reasonable proficiency in one additional area (e.g., frontend, backend, distributed systems, applied research, UI/UX/visual design).
- Prior experience in a fast-paced AI startup or high-growth technology company.
- Contributions to and experience with open-source projects.
How We Work
We are a small, early-stage team with big ambitions. We move quickly, care deeply about what we build, and take pride in doing things well. This is a high-ownership environment where people who are proactive, curious, and motivated by impact tend to thrive.
Why Join Us?
- Work in an environment conducting cutting-edge research in AI.
- Competitive salary, equity and benefits package.
- 28 days + public holidays allowance.
- Opportunities for professional growth and progression with your career.
- Work on challenging engineering problems that have a real impact on the industry.
- Work with high-profile customers and technology partners.
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