Machine Learning Engineer ( 70k+ + Equity) at SafetyMode
Machine Learning Engineer SafetyMode - Social impact 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 strategy and model training to production deployment, you will build real-time AI that identifies harmful content across all smartphone apps. This role offers the unique challenge of balancing high-performance computer vision with strict on-device privacy. Why This Role Is Remarkable
- Rare opportunity to own the end-to-end ML capability for a product with a massive social mission and high-profile backing from Sir Tim Berners-Lee and Hugh Grant.
- Work on cutting-edge technical challenges involving real-time, on-device inference (YOLO, Transformers, TFLite) where your code directly protects thousands of children from harm.
- High-impact founding team environment incubated by CanDo, with strategic partnerships including Mumsnet and Nothing Technology to scale 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 complex, high-noise mobile environments.
The Ideal Candidate
- At least 2 years of experience in applied ML with a track record of shipping production-grade computer vision 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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