Machine Learning Engineer

About the role

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 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

2+ years of engineering experience

Experience building and maintaining data pipelines and training workflows

Ability to take models from prototype to production deployment

Strong problem solving 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