Senior ML Engineer

We are representing an early-stage technology company building AI-driven systems to help detect and counter harmful information threats in real time. The company operates in a mission-critical problem space, combining machine learning, data infrastructure, and applied intelligence workflows to help users make faster, more reliable decisions.

This is a high-ownership environment suited to engineers who care about building robust production systems, not just experimenting with models.

This is an opportunity to join as a Senior Machine Learning Engineer and take ownership of production-grade ML systems from development through deployment, monitoring, and continuous improvement. You will work closely with a cross-functional team across engineering, machine learning, and intelligence-focused domains. The role is hands-on and systems-oriented, with a strong focus on reliability, scalability, and real-world performance. This is not a research-only position. The ideal candidate has a proven track record of shipping, operating, and improving ML systems in live production environments.

What You'll Do

• Build, deploy, and maintain production machine learning systems for detecting harmful or misleading information at scale.
• Own the full ML lifecycle, from data pipelines and model development through deployment, monitoring, and iteration.
• Design reliable and scalable ML infrastructure that supports both real-time and batch processing needs.
• Work with SQL and NoSQL databases to support data ingestion, storage, retrieval, and analysis.
• Implement clean, modular, maintainable Python code that can be extended by other engineers.
• Use containerisation, CI/CD, and cloud infrastructure to support production-grade deployment workflows.
• Evaluate technical trade-offs across latency, accuracy, cost, scalability, and performance.
• Collaborate with engineering, product, and domain specialists to shape both the product and the underlying ML architecture.
• Translate ambiguous, mission-critical problems into practical, working technical systems.

What We're Looking For

• Strong experience building and deploying machine learning systems in production environments.
• A clear track record of owning ML systems end to end, from data and models through deployment and monitoring.
• Strong Python engineering skills, with the ability to write clean, modular, maintainable code.
• Hands-on experience with CI/CD pipelines and containerisation tools such as Docker.
• Solid experience working with both relational and non-relational databases.
• Experience with large-scale data processing frameworks, including streaming and batch workflows.
• Broad exposure to different machine learning approaches and the judgment to apply the right method to the problem.
• Strong systems thinking, especially around reliability, scalability, latency, cost, and operational performance.
• A pragmatic, outcome-focused mindset suited to building real-world systems.
• Comfort working in a high-ownership, early-stage environment.
• Experience with NLP or machine learning systems related to content integrity, misinformation, trust and safety, or information analysis.
• Exposure to intelligence, security, geopolitical risk, or similarly complex data environments.
• Experience in an early-stage or high-growth startup.
• Familiarity with deep learning frameworks.
• Product-minded approach to ML engineering, with an interest in shaping both technical infrastructure and user-facing outcomes.

Why This Role Is Exciting

• Own meaningful ML infrastructure in a mission-critical and technically challenging domain.
• Work on production systems where speed, reliability, and accuracy have real-world importance.
• Join early enough to shape the architecture, engineering culture, and product direction.
• Collaborate with a highly cross-functional team spanning engineering, ML, and specialist domain expertise.
• Take on broad ownership across the full ML lifecycle rather than being limited to narrow model work.
• Solve complex problems involving real-time detection, large-scale data processing, and applied machine learning.
• Work in an outcomes-driven environment with flexibility and autonomy.

Work Model

This is a full-time, remote-first role based around London, with flexibility and occasional in-person collaboration or business travel expected.