What are the responsibilities and job description for the Founding ML Engineer position at Ensense AI?
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 founding 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
- 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 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