Role Description As Machine Learning / AI Engineer, you'll develop and integrate AI-driven features directly into the 3D visualization platform. Object placement, scene analysis, asset generation - you're applying machine learning to problems most engineers never get close to. - Design, develop, and deploy ML models for object detection, scene understanding, and 3D asset placement - Train and fine-tune models on 3D datasets to generate realistic visualizations while preserving image fidelity - Collaborate with graphics and full-stack teams to integrate AI models into the product pipeline - Implement tools to automate image segmentation, furniture recognition, and style recommendations - Optimize model performance for real-time or near-real-time inference at scale - Stay current on generative AI and ML advancements and bring relevant capabilities into the platform Qualifications - 3+ years in ML development with a focus on computer vision - Proficiency in Python and ML frameworks including PyTorch, TensorFlow, or Keras - Strong understanding of generative models (Stable Diffusion, GANs, VAEs) and LLM-based integrations - Experience with 3D data processing including point clouds, mesh recognition, and geometry analysis - Familiarity with object detection and segmentation tools (YOLO, Mask R-CNN) - Ability to deploy models efficiently using TensorRT, ONNX, or serverless cloud deployments Requirements - Experience with ControlNet for AI-driven image conditioning - Familiarity with 3D file formats and workflows (glTF, OBJ) - Experience with cloud-based ML platforms such as Vertex AI, AWS SageMaker, or Hugging Face - Background in recommendation systems for design suggestions or automated staging

• Lead operational improvement initiatives across the business. • Design scalable operational frameworks. • Improve cross-functional collaboration. • Standardize business processes. • Increase organizational efficiency. • Support strategic execution. • Document workflows. • Improve SOPs. • Build operational playbooks. • Optimize internal processes. • Improve quality assurance. • Reduce operational complexity. • Leverage AI to improve: - Documentation - Meeting management - Workflow design - Knowledge management - Reporting - Process mapping - Decision support - Operational analytics - Communication - Continuous improvement • Collaborate closely with: - Executive Leadership - HR - Product - Marketing - Sales - Partnerships - Customer Success - Engineering - Automation - Finance to ensure operational excellence across the organization. • Monitor KPIs. • Identify bottlenecks. • Improve operational visibility. • Support resource planning. • Drive accountability. • Continuously improve business performance. • Build better systems. • Improve documentation. • Increase automation. • Strengthen AI adoption. • Reduce manual work. • Scale operational excellence.

• Build AI agents, workflows, tools and automations that solve real customer, product and operations problems. • Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows. • Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence. • Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production. • Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust. • Turn manual processes into reliable AI-enabled systems that can scale.

• Build AI agents, workflows, tools and automations that solve real customer, product and operations problems. • Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows. • Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence. • Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production. • Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust. • Turn manual processes into reliable AI-enabled systems that can scale.