• Own the end-to-peer validation for existing algorithms • Visually validate data from cart collects for accuracy • Curate training examples to improve model performance • Utilize AI generative algorithms to create diversified training images • Set up tooling and scripts to handle data needs • Establish a semi-automated process to identify and leverage failure cases • Compare system production output against ground truth • Develop and maintain simple scripts to parse system logs

Become an EXpert as a Machine Learning Engineer. At EX Squared LATAM, we collaborate with global clients to build innovative digital solutions that drive real business impact. We are currently looking for a Machine Learning Engineer to join our team and support the development of advanced ML and GenAI solutions for one of our clients. This role offers the opportunity to work on cutting-edge technologies, including LLM evaluation frameworks, ML infrastructure, and Agentic AI systems, within a highly collaborative and fast-paced environment. What You’ll Do - Design and implement scalable ML infrastructure to support model development and deployment - Develop and maintain evaluation frameworks for Large Language Models (LLMs), including RAG-based systems - Evaluate model performance using tools such as RAGAS, DeepEval, or similar frameworks - Contribute to the development of GenAI and Agentic AI solutions - Build and enhance monitoring systems for ML models in production environments - Collaborate with cross-functional teams including data scientists, engineers, and product stakeholders - Apply best practices in model versioning, testing, and deployment pipelines - Optimize performance, scalability, and cost efficiency in cloud environments What You’ll Bring - 5+ years of strong experience with Python for machine learning and backend development - Hands-on experience with ML infrastructure and model deployment pipelines - Experience evaluating LLMs using frameworks such as RAGAS, DeepEval, or similar - Solid understanding of GenAI concepts, including LLMs and Agentic AI systems - Experience working with Retrieval-Augmented Generation (RAG) architectures - Proficiency in cloud platforms such as AWS, GCP, or Azure - Familiarity with monitoring and observability tools for ML systems - Ability to work independently in a remote, distributed environment - Advanced English level 🌎 Why Join EX Squared? At EX Squared, we are a team of passionate technologists across LATAM, building impactful solutions for global clients. We foster a collaborative, inclusive, and innovation-driven culture where continuous learning and professional growth are at the core of everything we do. What We Offer - Competitive compensation in USD - 100% remote work in LATAM - Access to continuous learning and development programs - A collaborative and multicultural work environment - Opportunities to work on cutting-edge technologies and global projects #LI-REMOTE

• Lead BEV model development: Define and execute the technical roadmap for BEV-based perception models across multiple tasks (e.g., detection, segmentation, road topology, and scene understanding). • Design advanced multi-modal architectures that fuse heterogeneous sensor data (camera, LiDAR, radar, HD maps) into unified spatial representations. • Develop foundational perception models leveraging BEV transformers, voxel-based encoders, or implicit scene representations. • Own large-scale training workflows — from data sampling strategies and augmentation pipelines to distributed training and hyperparameter optimization. • Advance model robustness and generalization, addressing long-tail conditions such as low visibility, occlusions, and rare scene configurations. • Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer performance. • Collaborate cross-functionally with sensor calibration, mapping, and fusion teams to ensure cohesive perception model interfaces. • Mentor and guide ML engineers, cultivating best practices in experimentation, code quality, and model validation. • Stay at the forefront of ML research, exploring self-supervised learning, large-scale pretraining, or foundation models for 3D perception.

• Design, train, and deploy machine learning models for image-based fraud detection, including deepfake detection, injection attack detection, and digital manipulation analysis in biometric verification • Work end-to-end across the ML lifecycle: dataset curation, model development and training, evaluation and iteration using fraud-relevant metrics, and production deployment and monitoring • Build robust data pipelines, including data validation, cleaning, and labeling strategies • Define and execute evaluation frameworks focused on real-world performance