Forterra is looking for a researcher/engineer to help design and develop methods that enable intelligent systems to make robust, explainable, and context-aware decisions, impacting the building of real-world AI capabilities for autonomous systems.
Requirements
- Proficiency in Python and modern deep learning frameworks (PyTorch, or TensorFlow)
- Strong foundations in machine learning and reasoning algorithms
- Experience with scaling and optimizing LLMs for efficiency (e.g., distillation, quantization, inference optimization)
- A strong background in transformer-based models and LLMs
- Hands-on experience with multimodal ML (e.g., vision-language, text + spatiotemporal data)
- Solid understanding of probabilistic methods, uncertainty modeling, or decision-making under uncertainty
- Proven ability to translate research into practical implementations
Responsibilities
- Research and prototype new methods to extend LLMs with reasoning capabilities
- Develop and optimize multimodal pipelines combining text, vision, and structured data
- Adapt and scale models for real-time, safe, and efficient performance
- Define evaluation strategies to measure robustness, reliability, and uncertainty in reasoning
- Work cross-functionally with research and engineering teams to bring ideas into production
Other
- Excellent problem-solving and collaboration skills
- Experience working on real-time or safety-critical AI systems
- Publications in top ML/AI conferences (e.g., NeurIPS, ICML, ICLR, CVPR)
- Contributions to open-source ML frameworks or toolkits
- Curiosity and drive to explore explainable and reliable AI reasoning
