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Spatial AI Engineer
BigGeo
Calgary, CAN
Full Time
Mid
Onsite
1 weeks ago
Machine LearningPythonPyTorchTensorFlowSpatial data processingSQL
Free
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About the Role
BigGeo is seeking a Spatial AI Engineer to build machine learning systems that operate on spatial datasets within The Spatial Cloud. You will design and implement models, pipelines, and inference services for spatial intelligence at global scale.
Key Skills for This Role
Machine LearningPythonPyTorchTensorFlowSpatial data processingSQL
Responsibilities
- Design, train, and iterate on machine learning models that operate on spatial and spatio temporal datasets.
- Build models that detect patterns, relationships, and anomalies across geospatial signals.
- Experiment with spatial reasoning approaches that incorporate location, geometry, and temporal context as explicit features.
- Evaluate model accuracy, calibration, reliability, and operational behavior against real production workloads.
- Build pipelines for ingesting, cleaning, transforming, and preparing spatial datasets for machine learning.
- Manage training datasets, versioning, and evaluation frameworks with the rigor of a production system.
- Deploy models into production systems used by applications, developer APIs, and AI workflows.
- Build inference services capable of delivering spatial insights in real time with predictable performance.
- Optimize AI models and inference pipelines for large spatial datasets and high throughput query patterns.
- Partner with Core Systems Engineers and data platform engineers to integrate AI capabilities with The Spatial Cloud.
- Own systems end to end: design, build, ship, measure, and improve.
Requirements
- 3 to 7 years of experience building machine learning systems or AI driven data products in production.
- Bachelor's degree in Computer Science, Engineering, or a related field.
- Strong programming experience in Python and deep familiarity with modern machine learning frameworks (PyTorch, TensorFlow, or equivalent).
- Experience building and deploying production machine learning models and inference systems, not just notebooks or prototypes.
- Hands on experience working with large datasets and distributed data processing pipelines.
- Solid grasp of machine learning evaluation, model lifecycle management, and responsible experimentation.
- Demonstrated ability to collaborate across engineering, data, and product teams and to own outcomes.
- Working knowledge of SQL and comfort operating in cloud native environments.
- Experience using AI development tools (such as Claude, ChatGPT, Cursor, and Copilot) to accelerate engineering work.
Full Job Posting
About BigGeo
- BigGeo is the Spatial Cloud, helping companies manage and access the world’s spatial data.
- We're building a new layer of the internet where the 'where' and 'when' behind every decision is instantly clear, programmable, and actionable.
Why BigGeo Exists and Why People Build Here
- Most companies are spatially blind; BigGeo exists to close that gap.
- We're not building another tool; we're building the rails that connect the planet’s moving data to the systems that run the world.
- People build here because the problem is real, the category is open, and your fingerprints are on the architecture.
The Role
- The Spatial AI Engineer builds systems that let AI models and applications understand and reason about the real world through spatial data.
- You will design and implement machine learning systems that operate directly on spatial datasets inside The Spatial Cloud.
- This role sits at the intersection of machine learning, spatial computing, and large scale data infrastructure.
What You Will Build and Own
- Spatially aware machine learning models that incorporate geometry, location, and temporal context as first class inputs.
- AI powered spatial analytics and pattern detection systems that find signal in global scale geospatial data.
- Spatial reasoning systems that understand how places, movements, and events relate across space and time.
- Training and evaluation pipelines for spatial AI models, including dataset management, labeling workflows, and reproducible experiments.
- Real time spatial inference services that deliver model outputs to applications and agents with low latency at scale.
- APIs and services that let developers, applications, and AI agents query spatial intelligence directly from The Spatial Cloud.
Key Responsibilities Spatial AI Model Development
- Design, train, and iterate on machine learning models that operate on spatial and spatio temporal datasets.
- Build models that detect patterns, relationships, and anomalies across geospatial signals.
- Experiment with spatial reasoning approaches that incorporate location, geometry, and temporal context as explicit features.
- Evaluate model accuracy, calibration, reliability, and operational behavior against real production workloads.
Data Engineering and Pipelines
- Build pipelines for ingesting, cleaning, transforming, and preparing spatial datasets for machine learning.
- Manage training datasets, versioning, and evaluation frameworks with the rigor of a production system.
- Ensure spatial data pipelines are scalable, reliable, and observable as datasets and usage grow.
AI System Integration
- Deploy models into production systems used by applications, developer APIs, and AI workflows.
- Build inference services capable of delivering spatial insights in real time, with predictable performance characteristics.
- Integrate AI capabilities directly with The Spatial Cloud’s data and compute infrastructure.
Performance and Scalability
- Optimize AI models and inference pipelines for large spatial datasets and high throughput query patterns.
- Make deliberate trade offs across latency, cost, accuracy, and operational complexity.
- Ensure spatial AI systems scale with growing datasets, growing users, and growing use cases without constant rework.
Collaboration and Ownership
- Partner with Core Systems Engineers building the spatial compute layer and data platform engineers managing large spatial datasets.
- Work closely with product teams to translate real customer problems into model behavior and service design.
- Own systems end to end: design, build, ship, measure, and improve.
Required Qualifications
- 3 to 7 years of experience building machine learning systems or AI driven data products in production.
- Bachelor's degree in Computer Science, Engineering, or a related field.
- Strong programming experience in Python and deep familiarity with modern machine learning frameworks (PyTorch, TensorFlow, or equivalent).
- Experience building and deploying production machine learning models and inference systems, not just notebooks or prototypes.
- Hands on experience working with large datasets and distributed data processing pipelines.
- Solid grasp of machine learning evaluation, model lifecycle management, and responsible experimentation.
- Demonstrated ability to collaborate across engineering, data, and product teams and to own outcomes, not just tickets.
- Working knowledge of SQL and comfort operating in cloud native environments.
- Experience using AI development tools (such as Claude, ChatGPT, Cursor, and Copilot) to accelerate engineering work.
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