Machine Learning Engineer

We are a VC-backed startup focused on hyper-personalisation, currently in stealth. Inspired by the latest in recommender systems, we leverage transformers and graph learning alongside decision-making models to build the most engaging customer experiences for in-store retail.

Our mission is to change retail forever through hyper-personalised experiences that are both simple and beautiful.

We are looking for a Machine Learning Engineer with strong software engineering fundamentals to join our team of domain experts and researchers. You will be responsible for building robust, scalable ML systems that bring our foundation models for retail from prototype to production.

• Design and build production-grade ML infrastructure, including training pipelines, model serving, and monitoring systems.
• Collaborate with research engineers to translate experimental models into reliable, maintainable software.
• Optimise ML systems for performance, scalability, and cost-efficiency in cloud environments (distributed clusters, GPUs).
• Establish engineering best practices for ML development, including testing, CI/CD, and code review standards.

• Month 1:
  Onboard to existing ML codebase and infrastructure; identify technical debt and reliability gaps; ship incremental improvements to model serving latency or pipeline robustness.
• Month 3:
  Own and deliver a major infrastructure component (e.g., feature store, training orchestration, or model registry); improve system observability with logging, metrics, and alerting.
• Month 6:
  Lead the end-to-end productionisation of our foundation model, meeting latency, throughput, and reliability SLAs; mentor teammates on engineering standards and contribute to architectural decisions.

• 3–5+ years building and maintaining ML systems in production environments
• BSc or MSc in Computer Science, Software Engineering, or a related field
• Strong software engineering skills: clean code, testing, debugging, version control, and system design
• Proficiency in Python with experience in ML frameworks (PyTorch, Tensor Flow, or JAX)
• Hands‑on experience with cloud platforms (AWS, GCP, or Azure) and containerisation (Docker, Kubernetes)
• Solid understanding of ML fundamentals (model training, evaluation, common architectures)

• Experience with MLOps tooling (MLflow, Kubeflow, Weights & Biases, or similar)
• Building data pipelines (real-time or batch) using tools like Apache Spark, Kafka, Airflow, or dbt
• Familiarity with recommender systems, transformers, or graph neural networks
• Exposure to model optimisation techniques (quantisation, distillation, efficient inference)

• Opportunity to build technology that will transform millions of shopping experiences.
• Real ownership and impact in shaping product and company direction.
• A dynamic, collaborative work environment with cutting‑edge ML challenges.
• Competitive compensation and equity in a rapidly growing company.