Permanent position: Senior Machine Learning Engineer

Senior Machine Learning Engineer
Remote (United Kingdom) | Permanent | Flexible compensation (DOE) + bonus + equity

We're working with a well-funded AI-first technology business building systems that go beyond traditional chatbot experiences and execute real-world workflows autonomously.

The team is building a proactive AI product designed to help users organise, communicate, and complete complex tasks with minimal prompting. The product focuses on long-running workflows, persistent context, and reliable real-world task execution, even when model behaviour is non-deterministic.

This is a hands-on engineering role focused on building and operating production machine learning systems rather than conducting pure research. You'll be responsible for translating research and modelling ideas into reliable systems used by real users.

The Opportunity

As a Senior Machine Learning Engineer, you'll own critical ML systems from end to end, taking ideas from experimentation through deployment, monitoring, and continuous improvement in production.

Areas of responsibility include:

• Building core ML systems that power AI-native product experiences
• Owning the full life cycle from data preparation and training through evaluation, inference, and iteration
• Turning research ideas into production-ready systems
• Debugging model behaviour using real-world production signals
• Improving accuracy, reliability, latency, and efficiency over time
• Working closely with product, research, and engineering teams to deliver user impact
• Mentoring other engineers through technical judgement and example
• Operating under real-world constraints including cost, performance, reliability, and safety

What They're Looking For

The team is interested in engineers who have built and shipped ML systems used by real users.

Examples of relevant experience include:

• Model training and evaluation
• Production inference systems
• PyTorch or JAX
• GPU-based training and inference
• ML experimentation and iteration
• Monitoring and debugging production ML systems
• AI reliability and performance optimisation
• End-to-end ownership of ML systems in production
• Applying research concepts to practical product problems

The strongest candidates typically demonstrate:

• Strong Python engineering skills
• Deep understanding of how ML systems behave in production
• Independent ownership and execution
• Excellent problem-solving ability
• A bias towards shipping, measuring outcomes, and iterating quickly

Technical Environment

• Python
• PyTorch/JAX
• GPU-based training and inference systems
• OpenAI, Anthropic and open-source models
• SQL/NoSQL
• Docker
• Kubernetes
• AWS, Azure or GCP

Compensation

Compensation is intentionally flexible and assessed based on experience, ownership, and production impact.

The package includes:

• Competitive base salary
• Performance-based bonus
• Equity
• Fully remote working
• Private healthcare
• Dental/Vision/Life insurance
• Pension contribution
• Paid holiday