What you will do

Technical Development & Innovation

• Design, implement, and optimize state‑of‑the‑art machine learning and computer vision models to enhance product capabilities.
• Research, evaluate, and apply modern architectures and techniques, including CNNs, transformers, and vision‑language models.
• Implement and benchmark newly developed algorithms on large‑scale datasets, validating both accuracy and throughput.
• Fine‑tune large‑scale models using efficient adaptation techniques such as LoRA and QLoRA.

Model Evaluation & Data Analysis

• Define, implement, and monitor appropriate evaluation metrics (e.g., precision, recall, ROC‑AUC, confusion matrices).
• Analyze training, test, and production data using statistical and visual techniques to identify performance gaps and reliability risks.
• Propose and implement data‑driven enhancements to model accuracy, robustness, and system stability.

Production Deployment & MLOps

• Support end‑to‑end ML workflows, including data preparation, training, deployment, monitoring, and iterative improvement.
• Contribute to CI/CD pipelines and production monitoring to ensure reliable, reproducible, and scalable model delivery.
• Assist in diagnosing and resolving model performance regressions and production issues.

Mentorship & Team Contribution

• Mentor and support junior CVML engineers across all phases of ML projects, including planning, data collection, annotation, training, deployment, and iteration.
• Participate in design reviews, technical discussions, and knowledge‑sharing initiatives to raise overall team capability.
• Contribute actively to Agile ceremonies and collaborative problem‑solving efforts.

Continuous Improvement & Collaboration

• Proactively suggest improvements to existing models, workflows, tools, and product features.
• Collaborate effectively with engineering, product, and data stakeholders to deliver high‑impact ML solutions.
• Maintain awareness of emerging ML and computer vision trends and assess their applicability to real‑world problems.

Skills we’re looking for

• Bachelor’s degree or higher in Computer Science, Electrical Engineering, or a related field or equivalent experience
• Strong hands‑on experience developing and deploying machine learning models in production environments.
• Advanced understanding of supervised, unsupervised, and semi‑supervised learning techniques.
• Expertise in classification, regression, clustering, and anomaly detection.
• Solid experience with convolutional neural networks, recurrent neural networks, and transformer‑based models.
• Strong proficiency in Python (C++ is a plus) and PyTorch (TensorFlow is a plus)
• Hands-on experience with modern neural network architectures and loss functions across tasks such as object detection, image segmentation, and representation learning.
• Experience using computer vision and scientific computing libraries such as OpenCV.
• Familiarity with model deployment, monitoring, and CI/CD workflows.
• Beneficial to have experience working with large‑scale datasets and performance‑critical ML systems.
• Prior experience mentoring or technically guiding other ML engineers.
• Beneficial to have exposure to production MLOps practices and model lifecycle management.
• Able to balance research‑driven exploration with pragmatic, production‑focused execution.

To find out more

As an equal opportunity employer, we are dedicated to creating a diverse and inclusive workplace where everyone feels valued and empowered. Please inform your GBG Talent Attraction Partner if you require any reasonable adjustments to the interview process.

To chat to the Talent Attraction team and find out more about our benefits and why we’re a great place to work, apply and we’ll be in touch.

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