[Hiring] Senior Machine Learning Engineer @Speedcast

Role Description

As a Senior Machine Learning Engineer, you will own the machine learning lifecycle on a modern lakehouse platform, including:

• Designing, building, evaluating, and deploying machine learning models — including time-series forecasting, anomaly detection, and classification — on network telemetry and business data.
• Building and maintaining feature and training-data pipelines on top of the data lakehouse (streaming and batch sources).
• Owning MLOps practices end-to-end: experiment tracking, model versioning and registry, CI/CD for models, automated retraining, and monitoring for drift and degradation.
• Defining and promoting standards for model development, evaluation, documentation, and governance across the team.
• Translating business problems into well-framed ML problems and communicating model behaviour, limitations, and business impact to stakeholders.
• Collaborating closely with data engineers to productionize models as reliable, observable services on AWS.
• Mentoring AI Engineers and data scientists on ML best practices, code quality, and evaluation rigour.
• Participating in Agile ceremonies, backlog grooming, sprint planning, and prioritisation activities.
• Evaluating and supporting the adoption of new ML technologies, tools, and architectural approaches.

Qualifications

• 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.

Requirements

• 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.

Benefits

• Global presence – work with clients and colleagues all over the world, in every continent.
• Talented teammates – co-workers are the best and brightest in the industry.
• Industry leadership – be part of the latest advancements shaping the future of communications.
• Ability to develop skills – work across departments to gain new skills and valuable experience.
• Choose your environment – select between a traditional desk job or work in the field.
• Grow your career – guide your career in the direction you want with internal promotion opportunities.
• Work on diverse projects – every day is different with customers from a range of industries around the world.