Job at a Glance
- 📍 Location: Dubai, Dubai, United Arab Emirates
- 🏢 Company: Zywa
- 💼 Employment Type: Full-time
- 📊 Seniority: Not Applicable
- 🗂️ Function: Engineering and Information Technology
- 🏭 Industry: Financial Services
- 📅 Date Posted: 2026-08-08
- 🌐 Category: Artificial Intelligence
About the Role & Company
Zywa has an open Applied ML Engineer role based in Dubai, Dubai, United Arab Emirates. You’d be plugging into a Artificial Intelligence team where the workload is growing, not shrinking. For candidates, that matters — moving into a function that’s investing tends to open doors faster than joining one on hold. The scope reads like a standard Applied ML Engineer brief on paper; in a growing team it usually stretches wider than the JD. Regionally, Dubai leads on both pay and visibility for artificial intelligence work; that hasn’t changed this year. Anyone at the mid-level should read this as a chance to broaden scope; anyone earlier in their career gets meaningful work sooner than most training paths would offer.
Key Requirements
- Years of this history sit in reports and carrier portals that nobody has ever built models on
- 2 to 6 years shipping ML systems to production, with the pipeline scars to prove it
- Real-world principles answer, under 60 seconds as a loom/Vimeo video, to: you have five years of claims history where every label is the approved value after negotiation between contractor and carrier
Job Description
Cozmo is the AI operating system for property claims. When a pipe bursts in someone’s home at 2am, our agents answer the call, capture the loss, enter the claim into Xactimate and Cotality, dispatch the right contractor against SLA, chase acceptances before breach and draft the carrier-ready estimate from field photos. We run both halves of a claims operation: everything the customer touches and everything that happens behind the desk.
Our customers are restoration franchisors, TPAs and adjusting firms whose boards have told them to become AI-native and who have no way to do it themselves. Our anchor is one of the largest restoration franchisors in the US.
Every claim we process generates hundreds of structured data points: line items, quantities, unit prices, room geometry, equipment counts, what was submitted and what the carrier approved. Years of this history sit in reports and carrier portals that nobody has ever built models on. Your job is to turn that raw sprawl into a data asset and ship the first models on top of it.
The pipeline is the hard part and the moat. Reports are messy, schemas drift across franchise locations, carrier responses arrive in half a dozen formats and the labels are negotiated outcomes rather than ground truth. The engineer who wins this role treats that mess as the job: builds ingestion that survives it, designs the schema the whole company will stand on and then trains the models, starting with gradient-boosted tabular systems predicting line-item approval outcomes and going wherever the data leads. Scope prediction from photos and transcripts, leakage detection and pricing intelligence are all open and unbuilt.
You own the whole path from raw export to a prediction serving a live claim. No handoffs, no research team upstream, no data team downstream.
You report to the CTO and work directly with both founders.
ATS Resume Tips for This Role
To get past Applicant Tracking Systems (ATS) for this Applied ML Engineer opening, tailor your resume with the pointers below:
- Include these keywords verbatim in your Skills and Experience sections: Python, LLM, SQL, TensorFlow, PyTorch, Machine Learning, Deep Learning, NLP, Computer Vision, MLOps. ATS parsers match exact strings, not synonyms.
- Use a single-column layout. Two-column resumes break parsers — text gets scrambled or dropped.
- Save as .docx or standard .pdf (not scanned/image PDF). Avoid text boxes, headers, footers, graphics.
- Use standard section headings: Experience, Education, Skills, Certifications.
- Mirror the exact job title (Applied ML Engineer) in your target role line where truthful.
- Quantify results with numbers, percentages, currency. Metrics improve ATS scoring and human review.
- Spell acronyms out once (e.g., Search Engine Optimization (SEO)) so parser catches both forms.
- Font: Arial, Calibri, or Times New Roman at 10-12pt. Fancy fonts can render as unreadable glyphs.
Top 10 Interview Questions for This Role
- Walk me through your background and why you’re interested in this role.
- Describe a challenging project you owned end-to-end. What was the outcome?
- Tell me about a time you disagreed with a stakeholder. How did you handle it?
- How do you prioritize when everything on your plate feels urgent?
- How do you decide between fine-tuning a model vs. prompt engineering?
- Explain overfitting to a non-technical stakeholder.
- Walk through your data validation pipeline for training data.
- How do you evaluate model performance beyond accuracy?
- Describe your MLOps setup — from experimentation to production.
- How do you handle model drift once a system is in production?
How to Apply
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