Machine Learning Engineer - Equity Only

PLEASE NOTE THIS IS AN EQUITY-ONLY ROLE AND THE INTERVIEWS WILL COMMENCE IN FEBRUARY 2025.

Stealth-Mode Start-Up Client is seeking a skilled Machine Learning Engineer to design, build, and deploy AI/ML models that power personalized recommendations, predictive analytics , and real-time data insights on a global platform. This role will focus on developing machine learning pipelines, fine-tuning algorithms , and ensuring scalable deployment of AI-driven features across both web and mobile environments .

The ideal candidate will have a strong background in machine learning, data science , and software engineering , with experience in building and deploying scalable AI systems .

To apply, please provide a CV, your compensation requirements (including salary expectations for when funding is secured) and a cover letter/note that explains why you are interested and how you meet the requirements. Please note that submissions received without all the requested information will be automatically disqualified and rejected.

Key Responsibilities:

• Design, build, and deploy machine learning models for tasks such as user behaviour prediction, content recommendations, and fraud detection.
• Develop and maintain end-to-end machine learning pipelines from data collection and preprocessing to model deployment and monitoring.
• Collaborate with Data Engineers and Product Teams to seamlessly integrate AI capabilities into product workflows.
• Build real-time ML systems capable of handling dynamic data streams and providing low-latency predictions.
• Fine-tune and optimize existing machine learning models to improve performance, scalability, and efficiency.
• Conduct A/B testing and model validation to measure the impact of deployed models.
• Implement robust model monitoring systems to detect drift, bias, and performance degradation in production.
• Collaborate with Data Engineers to ensure high-quality, preprocessed datasets for training and inference.
• Stay informed about emerging trends in AI/ML , exploring new technologies and techniques for potential integration.
• Create comprehensive technical documentation for models, pipelines, and experiments.

Requirements:

• Minimum 3+ years of experience as a Machine Learning Engineer , Data Scientist, or related role.
• Excellent command of the English Language in all forms.
• Previous start-up experience would be an advantage.
• Proficiency in Python, R , or Scala , with experience using ML libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience with tools like MLflow, Kubeflow , or similar platforms for managing ML pipelines.
• Hands-on experience with Hadoop, Spark , or distributed computing frameworks .
• Proficiency in SQL and NoSQL databases for accessing and preprocessing large datasets.
• Familiarity with cloud ML services (e.g., AWS SageMaker, GCP AI Platform, Azure ML).
• Experience deploying ML models in production environments using tools like Docker, Kubernetes , and CI/CD pipelines .
• Solid foundation in probability, statistics, and experimental design .
• Strong ability to work cross-functionally with Data Scientists, Engineers , and Product Teams .
• Analytical mindset with excellent problem-solving and debugging skills.

Ideal Candidate Profile:

• A passionate AI enthusiast who thrives on building systems that bridge innovation and user experience.
• Strong communicator, capable of explaining complex ML concepts to both technical and non-technical audiences.
• Adaptable and excited about solving real-world problems with AI and machine learning.
• Detail-oriented, with a strong focus on building scalable, efficient, and robust ML systems .
• Continuously curious about cutting-edge AI technologies and eager to experiment with new approaches.
• Collaborative mindset with the ability to work across teams and drive alignment .

Compensation & Benefits:

Equity-only at present, to transition to a salaried, full-time permanent position when funding is secured.

Remote and flexible working arrangements, the opportunity to be part of something potentially epic with potential opportunities for global travel, and access to industry conferences and workshops in due course.

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