Senior AI Engineer at well-funded safety tech startup

You will own the end-to-end lifecycle of AI models, from dataset strategy and fine-tuning to hardware deployment. By building robust, low-latency inference pipelines, you will transform real-world data streams into actionable insights for threat detection. This role bridges the gap between sophisticated machine learning research and high-performance edge computing.

Well-funded safety tech AI startup

• Design, train, and fine-tune text, audio, and vision models using frameworks like PyTorch, HuggingFace, and SpeechBrain.
• Optimize models for real-time performance through quantization, pruning, and engines like TensorRT or ONNX.
• Build and maintain reproducible MLOps pipelines including data collection, labeling, and automated CI/CD for model versioning.

• Proven expertise in Python and PyTorch with hands-on experience in audio/video AI stacks like YOLO or MediaPipe.
• Deep understanding of production MLOps, including experiment tracking, model registries, and containerized deployment via GCP.
• Skilled in optimizing low-latency inference for streaming systems and handling multi-objective metric trade-offs in safety apps.

• Lead the development of safety-critical AI models that have a tangible impact on real-world security and aggression detection.
• Join a well-funded startup backed by top-tier investors during a high-growth phase where you can influence the core technical architecture.
• Work at the cutting edge of MLOps, deploying sophisticated models to both cloud and specialized hardware at the edge.