About Inspekt AI
Inspekt AI, with its headquarters in Detroit, pioneers in the building inspection industry using artificial intelligence. Our platform utilizes advanced drone technology to capture high-resolution and thermal imagery, enabling precise and efficient inspections. Our solutions are designed to identify structural issues, energy inefficiencies, material flaws and safety risks, enhancing the sustainability and safety of buildings globally. When you work at Inspekt AI, you will get to work on some of the most famous and uniquely designed buildings in the world.

Inspekt AI builds computer vision that replaces slow, manual façade inspections with scalable, repeatable, image-based workflows. We deploy models into real customer projects, and we’re now scaling model quality, training reliability, and AI-driven inspection throughput.

You will lead the AI engineering function, responsible for our core perception stack:

High-resolution façade defect detection, image quality filtering, segmentation/classification of façade components, and 2D/3D data fusion into inspection pipelines.

This is not a research-only role. Your work must ship to production, be measured in live inspections, and directly improve the quality and efficiency of our customer deliverables.

Team stage & growth:

• We are expanding the team quickly
• You will be the technical anchor and player-coach who sets direction, upgrades the training/data pipeline, and scales the team into a high-output AI function.

Role split:

• ~80% hands-on technical work (coding, experiments, architecture, model iteration, PR reviews).
• ~20% technical leadership (mentoring, standards, roadmap input, hiring support).

You’re the person the team goes to when trade-offs are unclear, models underperform, or production behaves strangely

1) Hands-on AI / Computer Vision Delivery (~80%)

• Design, implement, and improve production-grade CV models for façade inspection, including:
  • Defect detection with precision-first targets.
  • Segmentation/classification of façade components and materials.
  • Image quality evaluation/filtering to improve downstream inspection accuracy and reduce noise.
• Own the end-to-end model lifecycle:
  • Data selection, preprocessing, and augmentation at scale (very high-res imagery, large project volumes).
  • Labeling strategy and training-specific QA in collaboration with annotators and façade engineers.
  • Training, validation, and evaluation using clear project-relevant metrics.
  • Batch deployment to production pipelines and continuous monitoring/improvement.
• Build and maintain a systematic experimentation engine: hypotheses, baselines, ablations, and clear readouts of what worked and why.
• Write production-quality code: modular Python, robust training/inference components, tests for critical paths, and clean integration with internal services/APIs.

2) Training Data Pipeline & Evaluation Foundations (Top Priority)

• Establish a high-quality AI training data pipeline that is distinct from human annotation workflows, including:
  • Dataset versioning and lineage.
  • Sampling strategy and coverage guarantees across projects/building types.
  • Label QA rules for training fitness (consistency, edge cases, class leakage).
  • Repeatable train/val/test splits and regression tracking.
• Create repeatable error-analysis workflows and dashboards tied to real project outcomes.

3) Technical Leadership & Mentoring (~20%)

• Act as the go-to technical expert for AI engineers: unblock others on architecture, training stability, debugging, and performance issues.
• Set and enforce standards for AI engineering:
  • Coding conventions, documentation, testing.
  • Reproducibility and traceability for models and datasets.
  • Experiment tracking discipline.
• Shape the AI roadmap with the technical management: recommend priorities based on impact, feasibility, and delivery constraints; clearly articulate trade-offs.
• Support hiring and onboarding of new AI engineers: interviews, technical assessment design, and structured onboarding to ramp quickly.

4) Model Strategy & Architecture

• Lead strategy for the model portfolio:
  • Decide when to use one generalized defect model vs. multiple specialized models (by building type, material, region, or inspection context).
  • Define decision criteria and rollout plan, including how models are selected per project.
• Define and refine requirements for scalable training and inference architecture, ensuring reliability and cost-awareness.

5) Collaboration & Stakeholder Management

• Work with façade engineers and delivery teams to:
  • Translate domain knowledge into useful guidelines, rules, and model objectives.
  • Validate that AI outputs are usable in real inspection workflows.
  • Establish a tight feedback loop that drives iterations and improvements.
• Work with cloud/infra engineers to specify training/inference requirements; they build the infrastructure, you ensure it meets model needs.
• Communicate clearly with leadership on progress, risks, hiring needs, and trade-offs.

Must-Have

• 5+ years hands-on ML/DL experience, with 3+ years in computer vision.
• Strong experience in image detection and segmentation using modern architectures (e.g., YOLO family, Mask R-CNN, UNet, transformer/ViT-based models).
• Proven track record taking CV models from prototype to production used in real projects and iterating based on monitoring + error analysis.
• Strong software engineering fundamentals:
  • Python, PyTorch/TensorFlow/JAX.
  • Clean, maintainable codebases; testing for critical paths; CI/CD literacy.
• Demonstrated ability to lead technically:
  • You’ve been a senior reference point, reviewed others’ work, guided technical direction, and mentored engineers.
• Comfortable defining success metrics from ambiguity and defending trade-offs with data.
• Bias toward shipping and measurable customer impact over shiny research.

Nice-to-Have (Domain Fit)

• Experience with very high-resolution imagery (40–60 MP) and large image volumes.
• Drone imagery, mapping, photogrammetry, or geospatial workflows.
• Building/infrastructure inspection, civil engineering, or similar domains.
• Familiarity with MLOps tooling (MLflow, W&B, SageMaker, Vertex, or equivalent).
• Experience building tooling/workflows for annotators, QA teams, or domain experts.

What We Offer

• A fully remote position, allowing you to work from anywhere in the Philippines
• Competitive salary and benefits package (PTO and HMO)
• Employee Stock Ownership Plan (ESOP) eligibility
• Flexible working hours to accommodate project needs and time differences.
• Opportunities for professional growth and development in a company at the forefront of AI-driven building inspection technology.