ML, Engineer
The posting does not state a salary. This range is our estimate from the role, the location and the stack — treat it as a guide, not an offer.
- Level
- Mid
- Apply from
- SA
About the role
- Design and develop machine learning models.
- Collaborate with cross-functional teams.
- Optimize models for performance and scalability.
- Implement feature engineering and validation techniques.
- Support enterprise AI initiatives.
Skills the posting asks for
- Machine Learning
- MLOps
- Data Science
Summarised from the employer’s posting, which is reproduced in full below.
The employer’s full posting
Master Works is seeking for a highly skilled Machine Learning Engineer to design, build, deploy, and scale machine learning models that power data-driven products and intelligent systems. The role sits at the intersection of Data Science, Software Engineering, and MLOps, requiring strong hands-on experience in transforming models into production-ready solutions.
The Machine Learning Engineer will work closely with Data Scientists, Product Managers, Software Engineers, and Data Engineering teams to develop scalable AI solutions, optimize model performance, and support enterprise AI initiatives aligned with engineering best practices.
Key Responsibilities
- Design, develop, train, optimize, and deploy machine learning models for real-world business use cases.
- Translate business and product requirements into scalable ML and AI solutions.
- Implement feature engineering, model selection, tuning, validation, and evaluation techniques.
- Develop and deploy ML models into production environments with high availability, scalability, and performance.
- Build and maintain machine learning pipelines including training, validation, deployment, and monitoring workflows.
- Monitor model performance, data drift, and model decay, and support retraining and optimization activities.
- Ensure ML solutions meet reliability, scalability, governance, and security standards.
- Collaborate with Data Scientists, Product Managers, Software Engineers, and Data Engineers across cross-functional teams.
- Support the development and maintenance of high-quality and reliable data pipelines.
- Participate in architecture discussions, design reviews, and code reviews following engineering best practices.
- Optimize models for latency, throughput, scalability, and operational cost efficiency.
- Implement experimentation and evaluation frameworks including A/B testing and offline evaluations.
- Apply Responsible AI principles including fairness, explainability, governance, and model transparency where applicable.