Senior Software Engineer – ML Data Platform

Role : Senior Software Engineer - Machine Learning Data Platform

Location : London (Liverpool Street)

Employment Type : Full-time and Permanent

Remuneration : £110-135k Base Salary + Discretionary Bonus + Equity

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 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.

In this role, you will:

• Design and develop a highly-scalable data platform capable of ingesting and processing hundreds of terabytes of training data.
• Design and develop methodologies and metrics to better understand the underlying quality, structure and distribution of training data.
• Architect training data validation, integrity and safety mechanisms for state-of-the-art ML models.
• Lead projects from inception to launch and beyond, having a high degree of autonomy and ownership over your work.
• Work closely with the founding team and contribute towards best practices, standards, and culture of the company.
• We use and leverage AWS as much as possible and manage it with Terraform.
• Services are written in Python and deployed to ECS or Lambda.
• We use Postgres, SNS/SQS and Redis.
• We have good end-to-end test coverage and are confident in our deployments.

What we are looking for:

• Back-end development: 5+ years of industry experience in back-end engineering developing data platforms or large-scale extract-transform-load (ETL) pipelines.
• Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker) and infrastructure-as-code software (e.g., Terraform).
• Algorithms and data structures: Excellent understanding of core CS fundamentals, including common abstract data structures and algorithms with the ability to apply them to optimise production systems.
• Strong ownership mindset: You naturally take responsibility for outcomes, not just tasks, and care deeply about the quality of what you ship.
• Product-oriented thinking: You think beyond implementation details, proactively suggesting improvements that balance customer value with business impact.
• Leadership : A track record of leading complex projects and influencing technical direction.
• Problem solving: Strong analytical problem-solving skills and attention to detail. You have the ability to break down complex problems into actionable tasks.
• Collaboration and communication: Excellent interpersonal and communication skills with technical and non-technical stakeholders.

We would like to acknowledge that almost no candidate checks every box - and that is perfectly fine. If you are passionate about data and enjoy solving complex challenges, we would love to hear from you.

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.

Nice to have:

• Machine Learning: Applied or theoretical background in machine learning and familiarity with fundamental concepts in the field.
• Open-source: Contributions to and experience in open-source projects.
• Startup experience: Experience with a startup work environment and wider ecosystem.
• 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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