Sr. AI Researcher, Agentic Systems

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.

Pay and benefits

The Base Salary range represents the low and high end of the anticipated salary range for this position across all US locations including but not limited to CA, CO, NY, WA, NV, MD, CT and RI.

The determination of this anticipated Base Salary involves the consideration of many factors in making compensation decisions including but not limited to: location of candidate, unique skill sets, experience, training, performance, licensure and certifications, as well as other business and organizational needs.

Our anticipated Base Salary determination is just one component of OpenSpace's competitive total rewards strategy that also includes a 401k match, as well as other region-specific health and wellness benefits.

Our US Benefits include

• 100% employer-paid medical, dental, and vision coverage
• Flexible paid time off
• 401(k) with company match
• Paid parental leave
• Home office stipend
• Employee referral program
• Annual company retreats and team gatherings

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