We help companies manage and access the world’s spatial data.
Any size, any slice, any insight.
Delivered in seconds.
The Spatial Cloud is the infrastructure layer that makes spatial data accessible to any company, at any scale, delivered in seconds. We help organizations manage and access the world's location data so their teams and AI systems can make big moves with confidence.
We're building something that hasn't existed before: a new layer of the internet where the "where" and "when" behind every decision is instantly clear, programmable, and actionable. Our platform removes the complexity that has kept spatial data locked in silos for decades — and replaces it with speed, precision, and control.
We're a Calgary-based company, early and moving fast, with real customers, real infrastructure, and a clear point of view on where the world is going.
Section 2. Why BigGeo Exists and Why People Build Here Most companies are spatially blind. They know what their data says, but not where or when things actually happen. That gap costs real money, creates real risk, and limits what AI can actually do in the physical world.
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. That’s a big problem, and it takes people who care about doing things right, not just fast.
People build here because:
The problem is real and the category is open. We’re not competing for the middle of an existing market, we’re defining a new one. Your work shapes what the category becomes.
Your fingerprints are on the architecture. We’re at the stage where the decisions you make today become the foundation tomorrow. What you ship matters.
We run on clarity, not politics. We move with purpose. No bureaucratic drag, no HiPPO decisions, just a team that agrees on the mission and gets to work.
You’ll grow fast because the problems are hard. Spatial data at scale is a genuinely difficult domain. If you want to be stretched, you’ll be stretched.
Section 3. The Role The Spatial AI Engineer builds the systems that let AI models, applications, and intelligent agents 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, turning raw location and time data into intelligence that applications and AI agents can act on in seconds.
This role sits at the intersection of machine learning, spatial computing, and large-scale data infrastructure. You will build the models, pipelines, and inference services that make spatial intelligence operational: not research artifacts sitting in notebooks, but production systems answering real questions at global scale.
You will work alongside Core Systems Engineers building the Spatial Cloud platform, data platform engineers managing global spatial datasets, and product teams shipping spatial intelligence capabilities to developers and enterprises. The problems are hard, the datasets are enormous, and the impact is visible, because what you build is used in real-world environments.
If you want to build AI systems that reason about the physical world and shape a new category of infrastructure as it takes form, this is the place to do it.
What You Will Build and Own As a Spatial AI Engineer, you will contribute to and own systems that include:
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 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, from dense urban data to sparse global datasets.
Experiment with spatial reasoning approaches that incorporate location, geometry, and temporal context as explicit features, not afterthoughts.
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, so intelligence lives where the data does.
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.
Preferred Qualifications Experience working with geospatial or location-based datasets in a production context.
Background in spatial analytics, geospatial modeling, or spatial statistics.
Familiarity with spatial indexing techniques (such as H3, S2, quadtrees, R-trees) and common geospatial data formats (GeoJSON, GeoParquet, PMTiles, FlatGeobuf, or similar).
Experience building AI systems that interact with structured data platforms, data warehouses, or lakehouse architectures.
Experience with real-time inference systems, streaming pipelines, or event-driven architectures.
Experience with performance‑critical programming in Rust or Go.
Contributions to open‑source AI, geospatial, or data infrastructure projects.
Technical Skills and Tools AI and Machine Learning PyTorch, TensorFlow, Scikit-learn.
Spatial machine learning frameworks and libraries.
Modern MLOps practices for training, evaluation, deployment, and monitoring.
Programming Languages Rust or Go for performance‑critical components.
SQL for data work and analytics.
Data Infrastructure Distributed data processing systems.
Cloud-native machine learning infrastructure.
Model training and deployment platforms.
Spatial Systems Geospatial data formats and standards.
Spatial indexing and query systems.
Large-scale spatial datasets and the workflows that manage them.
Operational Environment Slack, Google Workspace, Monday.
AI-assisted development tools embedded in day‑to‑day workflows.
Advanced AI Skills BigGeo is an AI-enabled engineering organization. Every Spatial AI Engineer is expected to use advanced AI tools as part of how they design, build, and ship work. This is not an optional layer, it is part of the job.
In this role, that means:
Using modern coding assistants (such as Claude, ChatGPT, Cursor, and Copilot) to accelerate implementation, refactor work, debugging, and testing.
Using AI to accelerate ML experimentation: drafting training scripts, generating evaluation frameworks, exploring alternative model architectures, and stress‑testing your own assumptions.
Engineers who thrive here treat AI as a force multiplier, not a crutch. We expect you to ship more, learn faster, and raise the bar of what a small team can build.

BigGeo is an in-office team. We build in person, think out loud, and move faster together than we ever could apart.
Section 5. Perks of Working at BigGeo We take care of the people who build here. That means keeping things easy, human, and worth showing up for.
Free refreshments and snacks, every day. The office is stocked, fuel yourself without thinking about it.
Downtown Calgary location. We’re right in the heart of the city, steps from a wide range of cafes, restaurants, and eateries. Lunch options are never a problem.
Regular off‑site team activities. We get out of the office and spend time together as a team, not as a box‑ticking exercise, but because we actually like the people we work with.

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📌 Spatial AI Engineer (Alberta)
🏢 BigGeo
📍 Alberta