Senior AI Researcher

Clariti Cloud Inc.

9h•CA$120,000 - CA$150,000•Remote

About The Position

Join our mission to provide governments with exceptional experiences so they can do the same for their communities! We empower governments to deliver exceptional citizen experiences. CivCheck is transforming how cities manage plan review and building code compliance. As part of Clariti, CivCheck brings together deep domain expertise and cutting-edge AI to streamline and modernize the permitting process. Our Guided AI Plan Review™ (GPR) platform is the first of its kind and it is helping cities and applicants alike navigate complex building codes with clarity, consistency, and speed. By combining intelligent automation with human expertise, CivCheck empowers communities to build faster, safer, and smarter. We're seeking a Senior AI Researcher who can translate the latest machine learning research into practical, domain-specific solutions in the permitting space. Working on the CivCheck platform, you'll own applied research and experimentation across computer vision, large language models (LLMs), multi-modal learning, and agentic AI workflows. This is a senior individual-contributor role on a small, fast-moving team - which means you won't be handing prototypes off to a large engineering org. You'll be expected to take your own projects all the way to production, working hands-on with our MLOps tooling and infrastructure. You'll also help define the work itself, turning vague product and business needs into clear research questions, concrete deliverables, and shipped systems. The ideal candidate has spent years doing applied research in industry settings, is comfortable owning the full lifecycle from idea to deployment, and is energized by hard, open-ended problems in a lean startup environment.

Requirements

7–10 years of hands-on experience in applied AI research, with a track record of shipping models and systems that solved real business problems
Demonstrated experience taking your own projects from research through to production — comfortable working hands-on with MLOps practices and infrastructure (CI/CD for models, containerization, monitoring, etc.) rather than relying on a large engineering team
A proven ability to operate independently and interface with nontechnical stakeholders – can take vague or under-specified problems and product needs, transforming them into scoped research plans and concrete deliverables
Deep, demonstrated experience training and fine-tuning models from scratch using modern frameworks — including defining layer-level architectures, curating & cleaning datasets, tuning training parameters, and diagnosing training/validation performance
Strong proficiency in Python and experience with PyTorch, TensorFlow, JAX, or similar deep learning frameworks
Hands-on experience with end-to-end ML workflows on cloud platforms such as AWS, Azure, or GCP, including deployment and monitoring
Strong software engineering skills and fluency with version control (Git), testing, and modular design
Significant experience implementing and improving core computer vision and image processing algorithms, including feature extraction, object detection, segmentation, and geometric transformations
Experience building agentic AI workflows and hands-on familiarity with LLMs, diffusion models, model distillation, or multimodal architectures in production
Sharp experimental and statistical intuition; comfortable making decisions from messy, incomplete results and identifying critical vs nice-to-have unknowns for further investigation
Excellent communication skills — you can align stakeholders, justify trade-offs, and explain research clearly to both technical and non-technical audiences

Nice To Haves

Experience developing computer vision systems requiring high accuracy and reliability under strict tolerances — for example, in autonomous vehicles, medical imaging, or industrial inspection
Preference for or comfort thriving in an early-stage or startup environment
Interest in the ethical implications of AI technology and responsible AI development and design
Exposure to the construction, building, civic-tech, or regulatory/compliance domains

Responsibilities

Own computer vision and agentic AI problem domains, selecting and adapting architectures and training/fine-tuning strategies for the best results on complex, unstructured real-world data
Drive the design and execution of experiments end to end, from framing the question to evaluating model performance and deciding what's worth pursuing
Help shape technical direction by identifying high-impact opportunities to improve our AI systems, drawing on applied experience and practical judgment
Make pragmatic trade-off calls across model accuracy, interpretability, latency, and cost, and directly own decisions in communication with stakeholders
Share your experience with research teammates and help raise the bar for experimental rigor in a collaborative environment
Stay current on techniques, benchmarks, and tooling across computer vision, generative AI, NLP, and multimodal learning — and bring the useful ones into practice