Applied AI Engineer

Senior Applied AI Engineer | Spatial Computer Vision & Digital Twins

Location: Fully Remote (in the US)

Salary: $170-230k DOE

 

About the Product

We build visual operations and maintenance software for complex industrial facilities, using high-resolution 3D digital twins to give engineers and technicians clear visibility into plants, substations, and other critical spaces.

Customers in the energy and industrial sectors use the platform to visualize infrastructure, centralize asset information and documentation, and support immersive training and work execution.

The Opportunity

We are building a new spatial AI engine that looks at 3D facility scans and automatically identifies every pump, valve, motor, and device, then matches each physical asset to the correct tags, drawings, and records in customer databases.

Today, utilities and industrial companies often spend years with large teams manually fixing asset tags and documentation. The goal is to remediate tens of thousands of tags in months and eventually scale to millions of assets across fleets of plants and substations with minimal manual effort.

This engineer will be one of the first focused on this initiative, working closely with technology leadership and the existing platform team to design and build the system from the ground up.

What You’ll Own

• Architect and build core components of the spatial AI engine, from ingesting 3D scene data to producing reliable, automatically generated asset tags and relationships that plug into a visual asset management platform.
• Design pipelines that transform 3D digital twins into representations suitable for asset detection, spatial reasoning, and downstream AI workflows.
• Develop and integrate computer vision models to detect and classify equipment in complex industrial environments, using techniques like object detection, segmentation, and 3D or spatial recognition.
• Build LLM and RAG workflows that reconcile detected assets with messy documentation, including drawings, PDFs, legacy asset registers, and tribal knowledge, creating high-confidence matches between the physical world and digital records.
• Implement robust backend services and APIs that expose these capabilities to 3D product experiences and visual work order workflows, collaborating with front-end engineers building immersive interfaces.
• Define metrics, validation frameworks, and feedback loops so the system can scale from tens of thousands of assets at early customers to millions of assets across broader portfolios.
• Work closely with customers and internal stakeholders to understand real-world constraints in power, industrial manufacturing, and other high-stakes environments.

Who You Are

We care more about the depth of problems you’ve solved than your exact background or years of experience.

• You’ve built complex production systems end-to-end in a demanding domain such as computer vision, robotics, crypto infrastructure, AR/VR, industrial software, or something similarly technical and unstructured.
• You are strong hands-on in Python and at least one modern web or backend stack such as TypeScript/Node, Go, or something similar.
• Computer vision or image-based machine learning is your primary strength, whether in 2D, 3D, or spatial environments, and you have trained and deployed models for detection, recognition, or scene understanding.
• You may also have strengths in LLMs, RAG pipelines, spatial computing, digital twins, 3D visualization, or building evaluation and monitoring systems around AI workflows.
• You are comfortable jumping into new domains quickly and learning the vocabulary and operating constraints of critical infrastructure.
• You like building real systems that are used in production, not just prototypes or research demos.
• You are energized by ambiguous, greenfield technical problems and enjoy operating in a small, fast-moving team.

Why This Role

• High-impact problem: this work has the potential to change how critical infrastructure is documented, maintained, and operated.
• Greenfield scope: this is a new product area with no established playbook, so this person will help define architecture, technical direction, and best practices from the beginning.
• Flexible profile: the team is open-minded on title, years of experience, and exact background for the right builder.
• Compensation flexibility: target compensation was initially discussed around the mid-to-upper 100s, but there is openness to go higher for the right person, especially for stronger computer vision talent.
• Team design flexibility: the company may hire one exceptional engineer or split the work across multiple hires depending on the skill mix they find.

Ideal Background

This role is likely to fit someone coming from one or more of the following areas:

• Applied computer vision
• 3D perception or spatial AI
• Robotics perception
• AR/VR or digital twin platforms
• LLM and RAG systems over messy enterprise data
• Full-stack or backend engineering in highly complex systems
• Crypto or other technically demanding startup environments where engineers build from scratch in ambiguity

Notes on Fit

The highest-priority need is someone with strong CV / image / spatial recognition ability. Secondary value comes from LLM / AI system experience. Generalist full-stack capability is also helpful, but it is the third priority behind the applied AI and perception side.

The right person does not need to come directly from digital twins or industrial software. Adjacent experience solving difficult, novel technical problems can be just as valuable if they are sharp, adaptable, and capable of building in ambiguity.