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.
About the Role
We are looking for a Senior Research Engineer with experience in advanced machine learning systems to work with our team of industry leading domain experts and engineers to build foundation models for retail shopping.
Key Responsibilities:
Translate latest ML advances into impactful solutions, validate their effectiveness, and deploy them to production systems.
Innovate upon state-of-the-art ML research applied to consumer personalisation.
Optimise machine learning models for performance in cloud environments (e.g., distributed clusters, GPUs).
Progression Timeline:
Month 1: Implement and evaluate current SOTA models, benchmark against existing internal models, and identify performance bottlenecks in existing implementations for optimisation.
Month 3: Design and implement a prototype model architecture, achieve quantifiable improvements in model performance metrics, and develop data pipeline components for large-scale training workflows.
Month 6: Deploy an optimised foundation model meeting production requirements, implement algorithmic improvements reducing training time and memory footprint, and document performance benchmarks with detailed ablation studies.
Essential Qualifications
3-5+ years implementing applied machine learning research in a production setting
MSc in Computer Science, Machine Learning, or a closely related field
Strong foundation in machine learning and deep learning algorithms (e.g., transformers, GNNs, supervised/unsupervised learning, reinforcement learning, deep neural networks)
Excellent Python programming skills with experience in developing and debugging production-level code
Desired Skills (Bonus Points)
Proven experience in recommender systems, behavioural AI, and/or reinforcement learning.
Building data pipelines (realtime or batch) & data quality using modern toolchain (e.g., Apache Spark, Kafka, Airflow, dbt).
PhD in Computer Science, Machine Learning, or a closely related field
What We Offer
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.
.png)
