Senior Machine Learning Engineer

As a Senior Machine Learning Engineer, you will play a leading role in designing and building novel AI-driven products at the edge of vision, interactivity, and real-time infrastructure. This is a hands-on position where you will implement models, write scalable code, and architect full-stack ML systems—from prototype to production.

Responsibilities

• Product-Focused Technical Leadership: Translate high-level product goals (e.g., real-time visual augmentations, analytics features, spatial reconstructions) into scalable and performant ML architectures—then build them.
• System Ownership: Design and develop complete machine learning systems, from data ingestion and model training to deployment and monitoring.
• Low-Latency Infrastructure: Build and optimise real-time inference pipelines that deliver live insights and on-the-fly visual augmentations.
• Strategic Collaboration: Partner with product and business teams to explore and prioritise new use cases for the core technology.
• Initiative Autonomy: Own and drive entire technical initiatives—from vague problem space to shipped, production-grade systems.
• Cloud-Native ML Engineering: Create reproducible workflows on platforms like AWS or GCP, including distributed training pipelines, inference optimisation, and domain-specific post-processing.
• Innovation at Scale: Push the boundaries of applied vision models while maintaining reliability and throughput across high-volume, global deployments.

Your Profile

• 5+ years of experience deploying ML systems in production settings (not just research or prototyping).
• Strong technical breadth: you can balance hands-on engineering with high-level product thinking.
• Experienced in computer vision: detection, segmentation, and ideally some 3D modelling or reconstruction.
• Self-directed problem solver—you’re comfortable owning ambiguous challenges and shaping solutions.
• Full-stack ML builder: from raw data to APIs, infrastructure to inference.
• Expert in PyTorch, proficient in multi-GPU setups, and cloud-native workflows.
• Experience with real-time systems and low-latency optimisation is a strong advantage.
• Enthusiasm for sports or spatial data applications is a plus.

Experienced Needed

• Proven experience building end-to-end ML pipelines in cloud environments.
• Strong system design and software engineering skills.
• Deep understanding of latency-critical systems and streaming architectures.
• Production-level ML deployment experience, including performance tuning and GPU optimisation.
• Skilled at leveraging modern dev tools (e.g., LLM-based assistants like Copilot or ChatGPT) for productivity and experimentation.

Nice to Have

• Exposure to 3D graphics, simulation, or AR/VR.
• Background in video processing, broadcast systems, or streaming technologies.
• Familiarity with sports data, interactive media, or real-time gaming environments.
• Open-source contributions or academic publications in ML or CV.