Applied ML Engineer (Computer Vision)

About CONXAI

CONXAI has built a no-code, agentic AI platform for the Architecture, Engineering and Construction (AEC) and physical industries, focused on knowledge-automation. We automate high-stakes, knowledge-intensive workflows traditionally trapped in siloed data, fragmented tools and tacit (undocumented) human expertise.  

Our multi-agent systems perform complex reasoning in the physical world; and transform bespoke, service-heavy processes into scalable Service-as-a-Software automation.

CONXAI is trusted by some of the leading AEC companies in Europe, US, LATAM and Japan.

Your Role

You bridge the gap between SOTA research and real-world deployments. You are responsible for ensuring Computer Vision models perform reliably when exposed to complex, unstructured customer data.

Core Responsibilities

• Own the Feedback Loop: Monitor production data to identify exactly where and why models struggle in specific customer environments
• Diagnose & Propose: Analyze discrepancies between model output and reality to propose concrete algorithmic or data-driven fixes
• Continuous Validation: Own the "last-mile delivery" by ensuring proper use-case setup and validating the accuracy of final results for the customer
• Drive Data-Centric Improvements: Lead the data "flywheel" by curating specialized datasets and integrating high-value customer data for model retraining
• Operationalize SOTA: Adapt high-level architectures into performant, cost-effective solutions tailored for specific customer use-cases
• Validate for Impact: Design evaluation frameworks that measure true customer value rather than relying solely on standard benchmarks

What We’re Looking For

• Bachelor's / Master's degree in Computer Science (or related) or Civil Engineering with specialization in Data Science / Lean Construction 
• Experience with training, and evaluating Deep Learning models in PyTorch
  
• Good understanding of basics in machine learning and computer vision, specifically representation learning
• Experience training and fine-tuning a Deep Learning architecture for:
  • Object Detection
  • Segmentation
  • Large Vision Language Models 
• Experience with data curation, visualization, outlier detection, active learning and hard-negative mining
• Proficient in Python and good software engineering skills
• Ability to work in a team-oriented environment
• Strong problem-solving skills
• Good communication and interpersonal skills (fluent in English)

Why CONXAI

• Edge of Innovation: Be at the absolute forefront of AI in the construction tech space
• High Autonomy: Contribute to a new paradigm for multi-modal scene understanding and reasoning - owning the logic, performance, and customer impact
• Top-Tier Peer Group: Work with a global team of ML engineers, software engineers and industry practitioners
• Equity & Scale: Competitive compensation with significant equity upside