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Added 52 mins ago
Oracle EPM Engineer
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Sr. AI Researcher, Agentic Systems
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The Sr. AI Research Engineer will build agentic systems that reason across the full OpenSpace data record: 360° imagery, point clouds, BIM models, drawing sets, and specifications, plus the…
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''
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OpenSpace

Sr. AI Researcher, Agentic Systems

Canada

Base Salary: $181,000 - $230,000 CAD

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Job details

Pay: Base Salary: $181,000 - $230,000 CAD
Posting language: English
Posted: Oct 29, 2026

Original job description

At OpenSpace, we’re redefining how the world’s most complex projects are built. Our AI-powered Visual Intelligence Platform uses computer vision and spatial AI to give construction teams a real-time view of what’s happening on-site, helping them build faster, safer, and with greater confidence.

But what truly sets us apart is our people. We hire curious, driven teammates who love solving hard problems, taking ownership, and making a real-world impact. Great people build great culture - and apparently it shows. Forbes has named OpenSpace one of America’s Best Startup Employers five years in a row. Come see what all the fuss is about ✨

The role:

The Sr. AI Research Engineer will build agentic systems that reason across the full OpenSpace data record: 360° imagery, point clouds, BIM models, drawing sets, and specifications, plus the newer streams coming off mobile capture, wearables, and field audio. Our computer vision team builds the perception primitives that determine what is in a capture. You will build the layer above, combining those primitives with frontier models to answer the questions project teams actually ask, and making the answers verifiable enough that a builder will act on them.

The work is applied research. The hard problems are deciding what an agent should see out of a four-dimensional site record, designing the tools it can call, and proving it is right often enough to ship. By the time you are calling a frontier API, the interesting decisions have already been made.

What you will be doing:

Design and build agent systems that plan and execute multi-step tasks across imagery, geometry, and documents.
Own the context layer. Build retrieval and grounding over a spatially and temporally indexed site record so an agent gets the evidence a question actually needs.
Design the tool surface: the primitives an agent calls to locate a component, compare two captures, read a region of a drawing, or query a BIM element.
Build evaluations for tasks where ground truth is ambiguous, then use them to make honest calls about what is ready and what is not.
Choose the right model for each step. Frontier API, open-weight model, fine-tuned small model, or classical CV, weighed against cost, latency, and accuracy at production volume.
Make outputs traceable. Every claim should point back to the pixel, sheet, or spec section that supports it.
Prototype quickly, kill what doesn't work, and partner with platform engineers to ship what does.

What we are looking for:

M.S./Ph.D. in a related field or equivalent industry experience.
5+ years building ML systems, including at least one LLM or VLM-based system you took to production and kept running. We will ask what broke.
Experience designing evaluations from scratch where ground truth is expensive, sparse, or contested.
Strong multimodal fluency: vision-language models, embeddings and retrieval, context design, and fine-tuning when it earns its cost.
Solid engineering. Python, async orchestration, tracing and cost instrumentation. You build your own harness.
Comfort with spatial data. You don't need SLAM depth, but coordinate frames, camera poses, and projections shouldn't slow you down.
Judgment about what is worth automating, and the willingness to say when the answer is nothing yet.

Nice to have:

Post-training or reinforcement learning on agent trajectories.
Document understanding over technical drawings, specifications, or other structured PDFs.
Sensor streams beyond core imagery, such as mobile capture, wearables, or field audio.
Experience in construction, robotics, geospatial, or another domain where models meet the physical world.

Base Salary: $181,000 - $230,000 CAD

OpenSpace welcomes employees from varied backgrounds and walks of life, and it’s reflected in our diverse community. OpenSpace is proud to be an equal opportunity employer and is committed to providing equal employment opportunities to all employees and applicants for employment, without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

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More about this job

Responsibilities

Design and build agentic systems that plan and execute multi-step tasks across complex spatial and document-based data. Develop the context layer and tool surface to enable agents to reason over imagery, geometry, and technical specifications.

Requirements

Requires an M.S. or Ph.D. in a related field and at least 5 years of experience building ML systems, including production-level LLM or VLM implementations. Candidates must possess strong multimodal fluency and comfort working with spatial data and coordinate frames.

Skills

Machine Learning
Computer Vision
Python
LLM
VLM
Spatial AI
Agentic Systems
Data Retrieval
Grounding
BIM
Point Clouds
3D Imagery
System Architecture
Evaluation Design
Multimodal Models

Visa sponsorship

Not detected in the job text

Keywords

AI Researcher
Agentic Systems
Computer Vision
Spatial AI
Machine Learning
LLM
VLM
Python
BIM
Point Clouds
3D Imagery
Data Retrieval
Grounding
System Architecture
Evaluation Design
Multimodal
Fine-tuning
Async Orchestration
Coordinate Frames
Camera Poses
Technical Drawings
Construction Technology
Robotics
Geospatial
Sensor Streams
Applied Research
Production Systems
Latency Optimization
Traceability
Model Evaluation

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