Job Opportunities API

Overview Stats Perform is the market leader in sports tech.  We provide the most trusted sports data to some of the world's biggest organizations, across sports, media, and broadcasting.   Through the latest AI technologies and machine learning, we combine decades' worth of data with the latest in-game happenings. We then offer coaches, teams, professional bodies, and media channels around the world, access to the very best data, content, and insights.  In turn, improving how sports fans interact with their favourite sports teams and competitions. How do we add value? Media outlets add a little magic to their coverage with our stats and graphics packages. Sportsbooks can offer better predictions and more accurate odds. The world's top coaches are known to use our data to make critical team decisions. Sports commentators can engage with fans on a deeper level, using our stories and insights. Anywhere you find sport, Stats Perform is there.  However, data and tech are only half of the package. We need great people to fuel the engine. We succeeded thanks to a team of amazing people. They spend their days collecting, analyzing, and interpreting data from a wide range of live sporting events. If you combine this real-time data with our 40-year-old archives, elite journalists, camera operators, copywriters, the latest in AI wizardry, and a host of 'behind the scenes' support staff, you've got all the ingredients to make it a magical experience! Responsibilities: We are looking for a Machine Learning Inference Manager to own the strategy, architecture, and day-to-day operation of our model serving platform. This is the person accountable for how models move from a trained artefact into reliable, low-latency, cost-efficient production inference- across real-time streaming, near-real-time, and batch workloads, on both on-premise GPU infrastructure and cloud. The role blends hands-on technical leadership with line management. You will set the technical direction for inference serving, define and defend performance and reliability SLOs, optimise GPU utilisation and unit economics, and grow a small team of ML platform/ inference engineers. You will act as the bridge between the research and applied-ML teams who produce models and the product and operations teams who depend on them being fast, available, and affordable. Key Responsibilities Inference Platform Ownership • Own the end-to-end inference serving stack: model packaging, deployment, versioning, routing, autoscaling, and decommissioning. • Define and maintain the reference architecture for serving models across on-premise GPU clusters and cloud, including the hybrid boundary between the two. • Establish standards and paved-road tooling so that model teams can deploy safely and repeatably without deep infrastructure knowledge. Performance, Latency & Optimisation • Set, measure, and enforce latency, throughput, and availability SLOs for each class of inference workload (real-time, streaming, batch). • Drive inference optimisation: batching strategies, quantisation, distillation-aware serving, kernel/runtime selection, and hardware-aware tuning. • Lead GPU efficiency initiatives - utilisation, memory management, multi-model serving, and right-sizing - to maximise throughput per unit of hardware spend. Reliability & Observability • Own the reliability posture of the serving platform: monitoring, alerting, capacity planning, failover, and incident response for inference services. • Implement observability for model latency, throughput, error rates, drift signals, and cost per inference, with clear dashboards for both engineering and leadership. • Establish rollout and rollback practices (canary, shadow, blue/green) so model updates ship safely. Cost & Capacity Management • Own the inference cost model and forecast; report unit economics (e.g. cost pe