Machine Learning Systems Engineer – Visual Data

Overview

In this role you will own core ML infrastructure end to end, turning research into scalable, production-ready platforms for large multimodal datasets. You will work closely with data and product teams to build reliable pipelines, optimize training and inference, and accelerate production deployments in a media-focused environment. The position emphasizes practical systems, performance, and cross-functional collaboration over pure research. This is a hands-on, product-minded engineering role with a strong focus on delivering real-world ML capabilities at scale.

Pay / Benefits

• Hybrid working

Responsibilities

• Build and evolve a data platform for large-scale multimodal datasets (LanceDB, DataFusion, SQL, vector search)
• Design ML pipelines for video indexing and processing (face detection, quality assessment, tracking)
• Improve training performance across single and multi-node setups using PyTorch and Ray
• Build evaluation and experimentation systems (Parquet/Iceberg) for model output analysis
• Own model versioning, lifecycle management, and promotion to production
• Optimise inference pipelines using Triton; build model ensembles and define request protocols

Key requirements

• Proven ML engineering background with a focus on infrastructure and productionisation
• Strong Python skills, plus experience with a robust production language such as C++ or Java
• Solid understanding of data pipeline performance trade-offs: I/O, compute, batching, memory layout
• Hands-on PyTorch experience: training pipelines, data loading, preprocessing
• Practical distributed systems experience (Ray, DDP, or similar)
• Experience handling TB-scale or high-throughput data pipelines
• Familiarity with columnar formats: Arrow, Parquet, Iceberg

• product-minded
• outcome-driven
• bridging experimentation and production
• Python
• C++ or Java
• PyTorch