Dentail AB jobb

Work at the core of what makes Dentail AI clinically credible — the detection models that identify pathologies and other findings in dental radiographs with accuracy that meets or exceeds specialist performance. You'll own the full MLOps lifecycle from research to production deployment and continuous evaluation. Dentail's AI engines are an ensemble of several ML models interacting through interconnected logical layers, so this role rewards both depth in modeling and systems thinking. What you’ll do Maintain and improve our training and evaluation infrastructure — data pipelines, annotation tooling, experiment tracking, and model versioning. Drive rigorous model evaluation — precision, recall, calibration, and per-pathology sensitivity/specificity trade-offs on real clinical data. Optimize model inference for production: latency, throughput, quantization, and hardware-efficient deployment. Contribute to research direction: what to build next, which problems are tractable, and how to measure success. What we’re looking for MSc in a relevant field (computer vision, deep learning, or similar); PhD welcome. 4+ years of machine learning experience focused on computer vision, with at least 2 years shipping models in production. Deep proficiency with PyTorch; experience with object detection frameworks (YOLO, DETR, or similar) and inference optimization (ONNX, TensorRT, quantization) a plus. Track record of designing or significantly modifying model architectures for specific problems — not just fine-tuning off-the-shelf models. Strong understanding of evaluation methodology — not just benchmark metrics, but sensitivity/specificity trade-offs in high-stakes classification. Familiarity with medical imaging (DICOM, radiograph modalities) is an advantage.

Own the business logic and infrastructure that moves dental radiographs from capture to AI analysis and back into the clinical record — reliably, securely, and at scale. You will be the primary architect of the platform, logic and API layers that every other part of our product depends on, including integrations with partners. What you’ll do Design and maintain the ingestion pipeline that connects Dentail AI to imaging hardware and practice management systems across the world. Build and operate the cloud infrastructure (Google Cloud) that runs our services to ensure high availability, scalability and smooth engineering operations. Drive reliability and observability across the platform — SLAs, alerting, incident response, and post-mortem culture. Work directly with ML engineers to optimise the path from model output to structured clinical data. What we’re looking for MSc, or similar, in computer science or a related field. 4+ years of backend engineering experience, with at least 2 years owning a production platform or data pipeline at scale. Strong proficiency in Python and/or Go; comfortable across the Google Cloud ecosystem Clear written communicator; our team is often async and documentation matters. Bonus: A track record of building and operating systems in regulated environments — financial services, healthcare, or similar.

Work at the core of what makes Dentail AI clinically credible — the detection models that identify pathologies and other findings in dental radiographs with accuracy that meets or exceeds specialist performance. You'll own the full ML lifecycle from research and training through to production inference and continuous evaluation. Dentail's AI engines are an ensemble of several ML models interacting through interconnected logical layers, so this role rewards both depth in modeling and systems thinking. What you’ll do Design custom model architectures for dental imaging — combining and extending convolutional, transformer-based, and multi-task approaches for detection of caries, periapical pathology, bone loss, and other findings across intraoral and panoramic radiographs. Maintain and improve our training and evaluation infrastructure — data pipelines, annotation tooling, experiment tracking, and model versioning. Collaborate with Dr. Alexander Johansson (CMO) and our medical advisory network to design clinically meaningful evaluation benchmarks and translate peer-reviewed findings into concrete model improvements. Drive rigorous model evaluation — precision, recall, calibration, and per-pathology sensitivity/specificity trade-offs on real clinical data. Optimize model inference for production: latency, throughput, quantization, and hardware-efficient deployment. Contribute to research direction: what to build next, which problems are tractable, and how to measure success. What we’re looking for MSc in a relevant field (computer vision, deep learning, or similar); PhD welcome. 4+ years of machine learning experience focused on computer vision, with at least 2 years shipping models in production. Deep proficiency with PyTorch; experience with object detection frameworks (YOLO, DETR, or similar) and inference optimization (ONNX, TensorRT, quantization) a plus. Track record of designing or significantly modifying model architectures for specific problems — not just fine-tuning off-the-shelf models. Strong understanding of evaluation methodology — not just benchmark metrics, but sensitivity/specificity trade-offs in high-stakes classification. Familiarity with medical imaging (DICOM, radiograph modalities) is an advantage.