Please submit your resume in English.

To learn more about our team and office culture in Cape Town, South Africa visit the following links. 
Careers Page: https://www.knowbe4.com/careers/locations/cape-town
Glassdoor: https://www.glassdoor.com/Location/KnowBe4-Cape-Town-Location-EI_IE969384.0,7_IL.8,17_IC2653748.htm
LinkedIn: https://www.linkedin.com/company/knowbe4/life/southafrica/

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 KnowBe4 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), PyTorch
• Data and Features: Apache Spark for distributed processing; experience with Feature Stores or automated feature engineering is a plus
• Infrastructure: AWS (SageMaker, Lambda), Docker, and Terraform/IaC for environment reproducibility
• Specialized Tooling: Experience with custom inference optimization (Python-based); orchestration via lean, custom AWS and Python-based solutions using Lambda and MLflow
•  Additional: C# and JavaScript (beneficial)
• Familiarity with secure coding practices

The Reality Check:

While the title says pure ML, the day-to-day is data heavy and is also focused on the AI and ML Platform Engineering around our services. Expect a roughly 90/10 split - the vast majority of your impact will come from hardening data pipelines, refining CI/CD for ML, and building world-class observability, with the remaining portion dedicated to model building, implementation and tuning.