Director of AI Engineering

Timescapes is looking for a Director of AI Engineering to join our small, but growing team of high-performers. We’re after someone who approaches problems from first principles, identifying underlying causes and diligently iterating towards the best solution. We believe that great products feature incredible experiences that customers never want to stop using. About us Founded in New Zealand in 2017, Timescapes is a visual progress tracking solution for complex construction projects. Our mission is to simplify construction through shared visibility. Customers love Timescapes because it helps them stay on schedule, validate construction claims and communicate progress more effectively. We’re a rapidly growing company, and are used by some of the largest construction firms across Australia, Canada, New Zealand and the United States. We’re deliberate and thoughtful in everything that we do, and we emphasise awareness, care and quality in our approach to product development. About the role As our Director of AI Engineering, you’ll be responsible for leading the engineering effort behind the AI capabilities that will shape the next generation of our product. This is a high-impact role for someone who can bring technical depth, structure and strong engineering judgement to an ambitious product area, while still being comfortable operating in the ambiguity of a startup environment. Key aspects of the role include: AI engineering leadership: leading the development of Timescapes’ AI systems, including computer vision, multimodal AI, model evaluation, data pipelines and production-grade AI infrastructure for both edge and cloud applications. Applied AI development: driving the engineering work required to turn early AI concepts into working product capabilities. Identifying ways in which off the shelf LLMs and LVMs can be combined with custom CV models to solve key construction problems. Technical judgement and architecture: providing strong engineering judgement across model performance, system design, data quality, observability, reliability, cost and scalability. AI evaluation and quality: building the processes required to understand whether our AI systems are working effectively. This includes defining evaluation approaches, performance benchmarks, acceptance criteria, failure modes and feedback loops. Cross-functional partnership: partnering closely with the Product, Software and Hardware teams to shape what is technically possible and also worth pursuing. Team building and management: helping define the right long-term path for the AI team. This may include hiring additional AI / ML engineers or other specialists, and determining where best to make use of external tools, models and partners