> Markdown version of [/jobs/ext/4490367-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/4490367-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Speedcast - **Location:** para, Spain (Remote available) - **Salary:** €70,000.0 - €80,000.0 - **Contract:** Permanent contract - **Skills:** Agile Methodology, Artificial Intelligence, Amazon Web Services, Amazon S3, Apache HTTP Server, Software Quality, Computer Programming, Databases, Continuous Integration, Database Queries, Python (Programming Language), Machine Learning, Scrum Methodology, Tensorflow, Data Streaming, Parquet, Pytorch, Large Language Models, Model Validation, Generative AI, Git, Cloudformation, Pandas, Scikit Learn, Real-time Inference, Apache Flink, Xgboost, Apache Kafka, Machine Learning Operations, Data Lakehouse, Terraform, Software Version Control, Data Pipelines, Docker, Confluent - **Published:** November 1, 2026 - **Apply:** https://www.adzuna.es/details/5947798124 ## About the Role * 5+ years of experience in machine learning, data science, or ML engineering, including delivering and operating models in production. * Strong programming skills in Python and deep familiarity with the ML ecosystem (e.g., scikit-learn, XGBoost, PyTorch or TensorFlow, pandas). * Strong SQL skills and experience working with large analytical datasets. * Hands-on experience with time-series modelling: forecasting and/or anomaly detection at scale. * Solid grounding in statistics, experiment design, and model evaluation methodology. * Hands-on experience with AWS services (e.g., SageMaker, Lambda, Step Functions, S3, ECR) and deploying ML workloads in the cloud. * Experience containerising applications using Docker. * Experience with MLOps tooling and practices (experiment tracking, model registries, monitoring). * Experience using Git for source control and working within Agile/Scrum environments. * Excellent communication and stakeholder management skills, including explaining model behaviour to non-technical audiences. Even better if you have * Experience applying ML to streaming data (Kafka/Confluent, Flink) or near-real-time inference. * Experience with lakehouse architectures (Apache Iceberg, Parquet, medallion/dbt patterns) and query engines such as Dremio. * Experience with time-series databases such as TimescaleDB. * Exposure to LLMs and generative AI systems, including evaluation of generative outputs. * Experience in telecommunications, satellite, networking, or IoT telemetry domains. * Experience with Infrastructure as Code (e.g., CloudFormation, Terraform) and CI/CD pipelines. * Advanced AWS expertise, including relevant AWS or ML certifications. * Experience mentoring engineers or leading technical workstreams. Working at Speedcast ## Description 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 prioritisation activities. * Evaluate and support the adoption of new ML technologies, tools, and architectural approaches.