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
ā¢ 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.
ā¢ 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.
ā¢ High-impact founding team environment incubated by CanDo, with strategic partnerships including Mumsnet and Nothing Technology to scale globally.
What You Will Do
ā¢ Design and train robust object detection and transformer-based text classification models to identify various categories of online harm.
ā¢ Optimize model architectures for low-latency execution on Android devices using quantization, TFLite, and novel on-device inference strategies.
ā¢ Establish rigorous evaluation suites and automated labeling pipelines to continuously reduce false positives in complex, high-noise mobile environments.
The Ideal Candidate
ā¢ At least 2 years of experience in applied ML with a track record of shipping production-grade computer vision or NLP systems.
ā¢ Proficiency in modern detection pipelines (YOLO) and transformer architectures (RoBERTa/ModernBERT), with a deep understanding of precision/recall tuning.
ā¢ Hands-on experience with on-device optimization tools like ONNX Runtime or Core ML and a passion for solving ambiguous, high-stakes safety problems.
Apply tot his job
Apply To this Job
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
ā¢ 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.
ā¢ 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.
ā¢ High-impact founding team environment incubated by CanDo, with strategic partnerships including Mumsnet and Nothing Technology to scale globally.
What You Will Do
ā¢ Design and train robust object detection and transformer-based text classification models to identify various categories of online harm.
ā¢ Optimize model architectures for low-latency execution on Android devices using quantization, TFLite, and novel on-device inference strategies.
ā¢ Establish rigorous evaluation suites and automated labeling pipelines to continuously reduce false positives in complex, high-noise mobile environments.
The Ideal Candidate
ā¢ At least 2 years of experience in applied ML with a track record of shipping production-grade computer vision or NLP systems.
ā¢ Proficiency in modern detection pipelines (YOLO) and transformer architectures (RoBERTa/ModernBERT), with a deep understanding of precision/recall tuning.
ā¢ Hands-on experience with on-device optimization tools like ONNX Runtime or Core ML and a passion for solving ambiguous, high-stakes safety problems.
Apply tot his job
Apply To this Job