Machine learning engineer (remote in south africa) new cape town, south africa

Cape Town, South Africa

Know Be4 is the global leader in Human Risk Management, trusted by over 70,000 organizations worldwide to secure their employees and AI agents for over 15 years. We're pioneering a new era of security. AI-powered since 2016.

And market-leading since day one.Our HRM+ combines continuous risk intelligence, advanced technical defenses, and personalized training to help organizations build strong security cultures. We help organizations understand, measure, and reduce human risk across their entire workforce, defending against, deepfakes, and emerging AI-powered threats.

We are looking for a Machine Learning Engineer to help scale the industrialization of our ML lifecycle. This is a Builder’s Role - you will be responsible for the high-scale architecture that allows our models to survive production. You’ll be a key contributor to our MLOps ecosystem, moving beyond experimentation to build high-throughput, distributed systems that serve as the backbone of our intelligence products.

Machine Learning Engineers help us extract value from our data. They are involved in all the processes from data collection, cleaning, and preprocessing, to training models and deploying them to production. This position will assess, analyze, and organize data, execute tests, and optimize the learning process to develop high-performance machine learning models.

Responsibilities Develops software using the Know Be4 Software Development Lifecycle and Agile Methodologies

Designs, develops, and researches Machine Learning systems

Transforms data science prototypes by applying appropriate Machine Learning algorithms and tools

Performs statistical analysis and using results to improve models

Inference Engineering: Drive the deployment and optimization of both standard predictive models and LLM architectures, balancing trade-offs between low latency, high throughput, and cost-efficiency

Platform Hardening: Transition research prototypes into resilient, production-ready microservices that can handle massive traffic

Lifecycle Orchestration: Execute automated pipelines for data and model versioning, validation, and retraining

Observability: Implement advanced monitoring for model drift, data integrity, and system health to ensure production reliability

Collaborative Standards: Uphold clean code practices, thorough documentation, and participate in rigorous code reviews across the ML and Engineering teams Requirements BS or equivalent plus 3 years experience

MS/Ph. D. or equivalent plus no experience

Training in secure coding practices (preferred)

AI/ML and Core: Python (production-grade), Py Torch

Data and Features: Apache Spark for distributed processing; experience with Feature Stores or automated feature engineering is a plus

Infrastructure: AWS (Sage Maker, Lambda), Docker, and Terraform/Ia C for environment reproducibility