Lead Machine Learning Engineer
Lead Machine Learning Engineer - Real-Estate Investment Platform π¦πͺ
Want to built a real-estate investment platform from scratch?
We’ve partnered with a well-funded, venture-backed startup building a smarter, more inclusive, real estate investment platform. They're bringing personalized, data-driven investment opportunities to users in a way that’s scalable, intuitive, and engaging. Machine learning is central to their product - powering everything from discovery and marketing, to decision-making.
You'll be coming in, setting up and leading the ML & AI function - you'll need to have experience building products from scratch and working in a fast-paced environment π
What You'll Get:
AED 50k (flexible) + shares + VISA + Medical
What You’ll Do
- Architect and build machine learning models for;
- Personalized investment recommendations
- Search relevance and ranking
- Lead scoring, conversion prediction, and dynamic pricing
- Audience segmentation and targeting for growth/marketing
- Own the full ML lifecycle: from data collection and preprocessing to model development, evaluation, deployment, and monitoring
- Introduce technologies and shape the roadmap for the ML function
- Collaborate closely with engineering, product, and marketing teams to translate business needs into ML solutions
- Set up scalable, maintainable ML pipelines and infrastructure from the ground up
- Explore and apply GenAI (e.g., embeddings, summarization) to enhance discovery and engagement for future growth
What We’re Looking For:
- 7+ years of experience in applied ML or data science, in tech or customer-facing products
- Strong understanding of classical ML techniques for regression, classification, clustering, and ranking
- Proven experience in building recommender systems, search infrastructure, or predictive models in production
- Extensive background and expertise within Python
- Previous roles where you have technically led and taken ownership in decision making, architecture and development
- Solid coding and software engineering practices (version control, CI/CD, testing)
- Strong deployment and monitoring experience
- End-to-end experience - building from the ground up
- Experience working with business and product stakeholders to deliver measurable results with ML
- Ability to balance technical depth with product impact
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