Senior Machine Learning Engineer

🕒 February 25

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Flatgigs

WebsiteLinkedInAll Job Openings

1 - 10 employees

Founded 2023

🎯 Recruiter

👥 HR Tech

Recruitment • HR Tech

Flatgigs is a Dubai-based talent and recruitment firm that partners with investor-backed and investor-ready MENA startups to fill critical revenue-driving roles. They provide end-to-end strategic talent acquisition, performance-focused cultural fit hiring, hyper-accelerated onboarding, and data-driven hiring optimization to maximize hiring ROI and extend runway for startups. Flatgigs positions itself as a B2B partner for high-growth companies and investors, embedding experienced leaders and teams to drive faster revenue milestones and reduce hiring risk.

📋 Description

• Build, train, and refine machine learning and deep learning models using time-series, sensor, and behavioral data. • Integrate data from wearables, fitness tracking platforms, and device APIs to create a clear story from movement, patterns, and activity signals. • Develop and maintain data pipelines that support both batch and real-time analytics. • Own model deployment in production environments — your models won’t live in notebooks; they’ll live in the world. • Work closely with engineering teams to integrate ML models into mobile and web apps. • Support logic for fraud, spoofing, and anomaly detection, ensuring data reflects real human activity. • Make complex outputs easy to understand — not just for engineers, but for product and business users too.

🎯 Requirements

• 5+ years of hands-on experience as an ML Engineer or Applied Scientist. • Strong foundation in machine learning, deep learning, and time-series analysis. • Experience working with wearables, IoT data, or sensor-based datasets. • Fluency in Python, PyTorch or TensorFlow, and good software engineering habits. • Experience building and shipping production ML systems using modern MLOps practices. • Comfort with Node.js, APIs, and backend integration workflows. • Understanding of data privacy, cloud ML infrastructure (AWS, GCP, or Azure), and edge inference. • A solid grasp of feature engineering, statistical reasoning, and evaluating what “good” looks like in a model.