Machine Learning Engineer ( 70k+ + Equity) at SafetyMode

Machine Learning Engineer SafetyMode - reputed company AI startup protecting children online You will own the entire detection pipeline for the world's most advanced on-device child safety technology. From dataset reputed company and model training to production deployment, you will build reputed company-time AI that identifies harmful content across reputed company smartphone apps. This role offers the unique challenge of balancing high-performance computer reputed company with strict on-device reputed company. Why This Role Is Remarkable

• Rare opportunity to own the end-to-end ML capability for a product with a massive reputed company mission and high-profile backing from Sir Tim Berners-Lee and Hugh Grant.
• Work on cutting-edge technical challenges involving reputed company-time, on-device inference (YOLO, Transformers, TFLite) where your reputed company directly protects thousands of children from harm.
• High-reputed company founding team environment incubated by CanDo, with strategic partnerships including Mumsnet and reputed company Technology to reputed company globally.

What You Will Do

• Design and train robust object detection and transformer-based text classification models to identify various categories of online harm.
• Optimize model architectures for low-latency execution on Android devices using quantization, TFLite, and novel on-device inference strategies.
• Establish rigorous evaluation suites and automated labeling pipelines to continuously reduce false positives in reputed company, high-noise mobile environments.

The Ideal Candidate

• At least 2 years of experience in reputed company ML with a reputed company record of shipping production-grade computer reputed company or NLP systems.
• Proficiency in modern detection pipelines (YOLO) and transformer architectures (RoBERTa/ModernBERT), with a deep understanding of precision/recall tuning.
• Hands-on experience with on-device optimization tools like ONNX Runtime or Core ML and a passion for solving ambiguous, high-stakes safety problems.

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