algo1 - Machine Learning Engineer
Requirements
• 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, TensorFlow, 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).
Responsibilities
• 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. • Progression Timeline • 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.
Benefits
• 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. • If you’re excited by the idea of shaping the future of retail and eager to make a real impact from day one, we’d love to hear from you.
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