Founding ML Engineer

Position Overview

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 creating a continuously updating Physical Intelligence Layer over streets through innovative multimodal sensing and advanced spatiotemporal AI systems. As a high caliber, early stage team of engineers, scientists, and operators, we value curiosity, engineering precision, and measurable impact.

In this role, you will be a founding machine learning engineer responsible for building and advancing the core intelligence that powers Ensense AI. Working directly with the founders, you will design models, build training pipelines, and deploy systems that interpret multimodal signals from the physical world, making a significant impact from experimentation to production deployment.

Key Responsibilities

• Develop and deploy machine learning models that interpret multimodal sensor, audio, video, and environmental data
• Build training pipelines, data processing tools, and evaluation frameworks for large scale spatiotemporal learning
• Fine-tune foundational models for perception, understanding, and inference in physical environments
• Collaborate closely with software and hardware teams to integrate models on device and in the cloud
• Prototype and validate new approaches for environmental understanding, anomaly detection, and physical world inference
• Design systems that ensure reliability, scalability, and high quality data
• Help define modeling strategy, architecture decisions, and long term research direction
• Contribute to a culture of engineering excellence, ownership, and speed

Required Qualifications

• M.Sc. or higher in computer science or a closely related field
• Deep understanding of machine learning fundamentals with the ability to innovate at the algorithmic level
• Expertise in signal processing techniques for audio or other sensor data
• Strong proficiency in machine learning frameworks such as Pytorch
• Experience with multimodal learning, especially computer vision
• Experience building and maintaining data pipelines and training workflows
• Ability to take models from prototype to production deployment
• Strong problem-solving skills and comfort in fast paced environments
• Clear and concise communication skills

Preferred Qualifications

• Experience with spatiotemporal modeling, sensor fusion, or geospatial data
• Background working with real world data from physical environments such as autonomous vehicle systems
• Experience deploying models on resource constrained systems
• Prior startup experience or history as an early technical hire
• High impact publications