Senior Machine Learning Engineer

Senior Machine Learning Engineer Senior Machine Learning Engineer Location: Remote- Spain: Candidates must be based in Spain and have the existing right to work in Spain. We are unable to offer visa sponsorship or relocation support for this role. Salary: EUR 70,000–80,000 per annum (depending on experience)] Overview of Position Speedcast delivers highly customizable, mission-critical communication solutions for remote sites worldwide. Our Data Engineering team operates a modern lakehouse platform — streaming telemetry from satellite and SD-WAN networks through Kafka into Iceberg, TimescaleDB, and a cloud query layer — that powers analytics across the business. As a Senior Machine Learning Engineer, you will own the machine learning lifecycle on this platform end-to-end: from framing problems with stakeholders, through model development and evaluation, to deployment, monitoring, and retraining in production. Typical problem spaces include network usage and capacity forecasting, anomaly detection on high-volume time-series telemetry, and predictive insights for service quality and cost. This is a senior role on a small, agile team. You will set the technical bar for how models are built, evaluated, and operated at Speedcast , and you will mentor AI Engineers on machine learning fundamentals and production discipline. The successful candidate works independently, proactively drives improvements, and communicates clearly with both technical and non-technical audiences. What you will do Design, build, evaluate, and deploy machine learning models — including time-series forecasting, anomaly detection, and classification — on network telemetry and business data. Build and maintain feature and training-data pipelines on top of the data lakehouse (streaming and batch sources). Own MLOps practices end-to-end: experiment tracking, model versioning and registry, CI/CD for models, automated retraining, and monitoring for drift and degradation. Define and promote standards for model development, evaluation, documentation, and governance across the team. Translate business problems into well-framed ML problems, and communicate model behaviour, limitations, and business impact to stakeholders. Collaborate closely with data engineers to productionize models as reliable, observable services on AWS. Mentor AI Engineers and data scientists on ML best practices, code quality, and evaluation rigour. Participate in Agile ceremonies, backlog grooming, sprint planning, and...

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