Machine Learning Engineer

About Ensense Ensense AI is building the next generation of Physical AI. Our mission is to bring transparency to public places through scalable sensing and software innovations that empower people, organizations, and governments to make better decisions. We are building a Physical Intelligence Layer over streets that is continuously updating, captured through innovative multimodal sensing and transformed into actionable intelligence by advanced spatiotemporal AI systems. Our work spans the full stack from sensing to intelligence, enabling a new class of real-time environmental, safety, and infrastructure insights. Ensense AI is a high caliber, early stage team of engineers, scientists, and operators who value curiosity, engineering precision, and measurable impact. Every team member is hands on and directly responsible for defining and advancing the state of the art in Physical AI. About the Role We are looking for a machine learning engineer to build and advance the core intelligence that powers Ensense AI. You will work directly with the founders to design models, build training pipelines, and deploy models that interpret multimodal signals from the physical world. This role is ideal for an ML engineer who enjoys solving real world problems, thrives in early stage environments, and wants meaningful ownership of both the experimentation and production deployment of advanced models. Responsibilities Develop and deploy machine learning models that interpret multimodal sensor, audio, video, and environmental dataBuild training pipelines, data processing tools, and evaluation frameworks for large scale spatiotemporal learningFine-tune foundational models for perception, understanding, and inference in physical environmentsCollaborate closely with software and hardware teams to integrate models on device and in the cloudPrototype and validate new approaches for environmental understanding, anomaly detection, and physical world inferenceDesign systems that ensure reliability, scalability, and high quality dataHelp define modeling strategy, architecture decisions, and long term research directionContribute to a culture of engineering excellence, ownership, and speedRequired Qualifications M.Sc. or higher in computer science or a closely related fieldDeep understanding of machine learning fundamentals with the ability to innovate at the algorithmic levelExpertise in signal processing techniques for audio or other sensor dataStrong proficiency in machine learning frameworks such as Pytorch2+ years of engineering experienceExperience building and maintaining data pipelines and training workflowsAbility to take models from prototype to production deploymentStrong problem solving and comfort in fast paced environmentsClear and concise communication skillsPreferred Qualifications Experience with spatiotemporal modeling, sensor fusion, or geospatial dataBackground working with real world data from physical environments such as autonomous vehicle systemsExperience deploying models on resource constrained systemsPrior startup experience or history as an early technical hireHigh impact publications