Applied AI Engineer

About Foresight Foresight's mission is to bring the world's critical infrastructure into service faster by better enabling teams to deliver complex capital projects. The AI buildout runs on physical things: data centers, power, and the supply chains behind them. A single hyperscale campus involves an owner, a general contractor, dozens of trades, long-lead equipment suppliers, lenders and commissioning teams. Each works from its own schedule and its own spreadsheets. When a project slips, the people who could fix it usually find out last. For an AI data center, a month late can mean tens of millions in lost revenue. Foresight is the control tower for these projects. We connect schedules, field progress and execution data across every party, forecast where delivery is heading, and help teams act before a delay becomes a missed date. On one 15-project portfolio, Foresight found a project 245 days behind, cut reporting overhead by 90%, and helped prevent more than $100M in lost revenue. We work with top data-center owners, developers and general contractors across the world, including Crusoe, Applied Digital, and NTT. We raised a $25M Series A led by Macquarie Capital and are headquartered in New York. The role We're looking for an Applied AI Engineer to help build the core ML engine of the Foresight platform. You will build the systems that enhance the ability for efficient delivery of large scale construction projects. You will architect, train and deploy the models that perform Foresight's risk predictions and AI recommendations, to be used in interfaces that make 100k-activity projects understandable at a glance. This is a hands-on role on a new team, for someone who owns models and backend features from training to deployment and monitoring to build systems that are accurate, scalable and observable. At Foresight, machine learning is an integral part of the platform. You will be using complex, multimodal high stakes data to make predictions that will be used by teams to deliver major projects on time. This could translate to work on: Risk prediction: Build, train and deploy models that predict if a project will be at risk and by how much. Criticality prediction: Build models that use project plans to identify which of the tasks are the most critical to complete the project on time before the project even starts. Project planning: Build models that accept blueprints and 3D Replit models to create project timelines and scaffolding as the starting point for human planners to make project planning a breeze. Progress trackers: Build models that take in multi-model data from static cameras or videos from humans/drones on-site, voice notes, etc about a task to predict what percent of the task is complete. Risk mitigation: Build, train and deploy models that can predict the best path forward for risk mitigation or timeline amelioration for major projects. Continuous improvement: Evaluate models repeatedly and prototype methods and new models to improve the accuracy of current features, inspired by latest research publications. What you'll do Own features from technical design through release and beyond: measure adoption, learn and keep improving. Work alongside Full Stack engineers to help them turn new models into production features customers rely on. Partner with product, design and customers to find the problems most worth solving. Tune performance across the modelling stack, from accuracy to latency and reliability. Raise the bar on architecture, testing, observability and how the team ships. Help shape the product and technical roadmap. Requirements You might be a fit if you Have 3+ years of experience building and shipping Machine Learning models in production, with real ownership of the backend. Have 1+ years of experience building and shipping VLM/LLM based features to production. Are strong in Python on the backend (FastAPI, Django or similar). Are solid at relational data modelling in Postgres: schema design, indexes, que…

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