Machine Learning Engineer

• Develop and improve real-world computer vision systems, with a strong focus on aerial image recognition
• Build solutions involving object detection, image segmentation, classification, and pattern recognition
• Train, fine-tune, evaluate, and deploy computer vision and machine learning models
• Experiment with modern vision models, foundation models, and multimodal AI technologies
• Research new AI approaches and evaluate their potential value for the product
• Rapidly build prototypes and validate technical ideas
• Improve model accuracy, reliability, performance, and inference efficiency
• Work with visual data and develop data preparation, training, and evaluation pipelines
• Explore LLMs, embeddings, vision-language models, and other emerging AI technologies
• Use AI coding tools and agents to accelerate development, research, debugging, and prototyping
• Identify ways AI can improve the overall product and user experience
• Take ownership of technical challenges from initial research through implementation and deployment
• Work directly with the client’s team in an international environment
• Collaborate in a fast decision-making, minimal-bureaucracy culture with direct access to the co-founder

AI/ML Engineer

• Develop full-stack machine learning models, including binary classification and regression, for financial crime detection and prevention during transaction processing and client onboarding
• Gather requirements, prototype, and deploy models in production
• Own and evolve the Dynamic Risk Score (DRS) across all markets, scoring the full client base daily
• Support decision-making across the client lifecycle, including quarantine, alert suppression, and future credit decisions
• Manage transaction rules with ML-driven detection and optimize performance and alert volumes
• Establish continuous production monitoring for model and rule performance
• Develop a champion-challenger framework to keep the best-performing model live
• Participate in infrastructure development for ML operationalization and inter-service interactions
• Serve and infer models of varying complexity

Senior Machine Learning Engineer

• Design and build core machine learning systems for a proactive AI platform
• Own projects end-to-end, including data curation, model training, evaluation, inference, and continuous iteration
• Translate cutting-edge research concepts into robust, reliable production systems
• Use live production signals and data to troubleshoot model anomalies and system bottlenecks
• Deploy systems, analyze real-world performance metrics, refine approaches, and iterate rapidly
• Partner with research, engineering, and product teams to deliver user value
• Mentor fellow engineers and conduct rigorous reviews
• Set a high standard for technical judgment through hands-on execution
• Optimize model architectures for latency, computational cost, reliability, and safety at scale