What are the responsibilities and job description for the Founding Data Infrastructure 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 government 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 data infrastructure engineer to design and build the systems that move, store, and manage the multimodal data that powers Ensense AI. You will work directly with the founders to architect the data backbone of our Physical Intelligence Layer, enabling real time understanding of the physical world through street level sensing. This role is ideal for someone who enjoys building high performance data systems, thrives in early stage environments, and wants meaningful ownership of core infrastructure that will scale to millions of daily observations.
Responsibilities
- Architect and build data pipelines for large scale multimodal streams including video, audio, and sensor telemetry
- Design and maintain high performance storage systems optimized for spatiotemporal retrieval
- Build tools and services for ingestion, transformation, labeling, and quality control of physical world data
- Develop internal data platforms that support analytics, model training, and real time intelligence
- Collaborate closely with software, machine learning, and hardware teams to ensure seamless data flow from devices to cloud
- Implement best practices that ensure reliability, scalability, and quality
- Define long term data architecture and contribute to foundational engineering decisions
Required Qualifications
- 5 years of professional experience working with large scale data systems
- Strong proficiency with distributed systems and modern data infrastructure
- Experience building large scale data pipelines using tools such as Kafka, Spark, Flink, Ray, or similar
- Proven expertise of data modeling, storage systems, and performance optimization
- Ability to design, operate and optimize cloud systems on AWS or GCP
- Strong problem solving and comfort in fast paced environments
- Clear and concise communication skills
Preferred Qualifications
- Experience working with high volume real world data such as autonomous vehicle datasets
- Interest in Physical AI, smart infrastructure, or urban systems
- Background in early stage startups or experience as an early technical hire
- Experience with spatiotemporal indexing, geospatial data storage, or mapping frameworks