Senior ML Engineer - Personalisation

Beyond is a technology consultancy helping organizations thrive in a rapidly changing world.

We build, modernize, scale, and operationalize technology, creating Cloud and AI solutions to unlock productivity and drive customer growth.

Role Overview

• We're looking for a driven and versatile engineer to shape the next generation of intelligent personalization systems. This role offers the opportunity to contribute towards architectural strategy, transformative initiatives, and work at scale on personalization platforms infused with machine learning and semantic intelligence.

As a Senior ML Engineer at Beyond, you’ll:

• Develop scalable, high-performance personalization backend systems and engines, including the ingestion and processing of data to create AI driven personalization pipelines.
• Bring to life industry leading personalization technologies, leveraging traditional and advanced Machine Learning models, as well as large language models (LLM) to improve user experience through personalization.
• Implement approaches to enhance the performance, reliability, and observability of personalization services, ensuring low-latency, high-availability systems.
• Implement frameworks for evaluating personalization quality through both offline metrics and live A/B experimentation.
• Champion engineering best practices and mentor engineers across teams, raising the bar for code quality and system design.
• Staying ahead of trends in personalization and recommendation technologies, distributed systems, and bringing these innovations into production.

Things that will make you stand out:

• Degree in Computer Science, Engineering, or a related technical field.
• 6+ years of experience in developing large-scale distributed backend systems.
• Hands-on experience with personalization infrastructure and/or recommendation engines is a strong advantage.
• Deep expertise in Collaborative Filtering (user-item, item-item), Content-Based Filtering, and Matrix Factorization techniques (SVD, ALS).
• Experience developing advanced models, such as:
• Deep Learning Architectures: Including Two-Tower models for scalable candidate retrieval, and sequence-aware models like Transformers or RNNs for session-based recommendations.
• Hybrid Models: Combining multiple approaches (e.g., collaborative and content-based) to overcome their individual limitations.
• Developing specialized Techniques, such as:
• Multi-Armed Bandits (for exploration vs. exploitation)
• Learning to Rank (LTR) for optimizing ordered lists
• Generating embeddings for users and items
• Mastery of Python and its core ML ecosystem, including TensorFlow, PyTorch, Scikit-learn, and XGBoost.
• Demonstrable experience building robust APIs (REST, GraphQL) and operating in modern cloud environments (GCP, AWS), using Kubernetes, Docker, CI/CD, and observability tools.
• Proven ability to influence engineering direction across teams and functions.
• Strong communication skills, being able to engage diverse technical stakeholders.

Nice to Have

• Specialisation in recommendation-related ML areas such as deep Learning for sequential Data, reinforcement learning, causal inference, ML System Architecture and GNN (Graph Neural Networks).
• Experience integrating LLMs, developing custom model architectures, or deploying machine learning solutions in production to enhance personalization relevance.

Having been named among the Sunday Times Best 100 Companies, we believe culture plays a large role in what we offer as an organization. We actively promote diversity in all its forms across our Studios, and we proudly, passionately, and proactively strive to create a culture of inclusivity and openness for all our employees.

Beyond is committed to welcoming everyone, regardless of gender identity, orientation, or expression. Our mission is to remove exclusivity and barriers and encourage new thinking and perceptions in a space of belonging. It is not about race, gender, or age, it is about people. And without our people being their most creative and innovative selves, we are nothing.