Company Description Leni is an AI infrastructure platform built for asset managers and investors, designed to help individuals and organizations make confident, data-driven decisions. The platform is model-agnostic for most tasks and also uses specialized models trained on real estate and investment workflows to provide highly accurate, cost-efficient results. Leni has achieved top global performance on industry-relevant benchmarks such as DRACO, SpreadsheetBench, and GAIA, and consistently delivers traceable, verifiable outputs that stand up to investor and investment committee scrutiny. By connecting to systems like Yardi, RealPage, Entrata, AppFolio, and Rent Manager, Leni builds a private context graph that captures the meaning behind data across assets, operators, and market cycles. This growing memory layer powers insights on trends, risks, and operational work, acting as an always-on, context-focused teammate for clients.
Role Description The AI/ML Engineer will design, develop, and deploy machine learning models and infrastructure that power Leni’s real estate and investment workflows. Day-to-day responsibilities include building and optimizing algorithms for pattern recognition and decision support, integrating models with data pipelines and third-party systems, and improving accuracy and robustness through experimentation and evaluation. The engineer will collaborate closely with product and engineering teams to translate domain requirements into scalable ML solutions, monitor performance in production, and implement enhancements based on user feedback and benchmark results. This is a full-time hybrid role based in Toronto, ON, with a combination of in-office collaboration and the option to work from home for part of the week. The role involves hands-on coding, system design, and contributing to best practices for reliable, traceable AI in a high-stakes financial and real estate environment.
Qualifications
- Strong foundation in Computer Science and Algorithms, with experience designing efficient, scalable solutions for production systems.
- Proficiency in Neural Networks and Pattern Recognition, including building, training, and tuning models for real-world data and complex decision tasks.
- Solid understanding of Statistics and statistical modeling, with the ability to evaluate model performance, handle uncertainty, and design robust experiments.
- Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow), data processing tools (e.g., Python, SQL), and cloud or container-based deployment environments.
- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent practical experience.
- Experience building or maintaining ML systems in finance, real estate, or data-intensive enterprise environments is an asset.
- Ability to work effectively in a hybrid setting in Toronto, ON, collaborating with cross-functional teams and communicating complex technical concepts clearly.
- Commitment to responsible,
