Evident is changing how dental clinics and labs operate—digitally and intelligently. Our flagship platform, EviSmart™, empowers dental professionals through smarter workflows powered by AI. We're not here for theoretical research—we're building real AI for real users in a high-impact industry.

We're looking for a Machine Learning Engineer who loves shipping real-world AI features. This isn't a role for ivory-tower researchers—we want someone who can take an idea from notebook to production. You'll build models that work with multimodal data—images, clinical text, 3D dental files—and make them run smoothly in a production SaaS platform.

If you're a builder, a problem-solver, and someone who thrives on seeing your work in the hands of users—this one's for you.

• Design, train, and deploy machine learning models for classification, detection, and multimodal tasks
• Work with 3D scans (STL), dental images, product specs, and clinical notes
• Fine-tune models like CLIP, BLIP, open LLMs, and vision transformers for practical use
• Develop production-grade ML pipelines—data preprocessing, evaluation, monitoring, and inference
• Collaborate closely with product, design, and full-stack teams to ship fast and iterate
• Monitor and improve model performance in real-world conditions

• 3+ years of experience in machine learning or applied AI roles
• Strong programming skills in Python, with experience using PyTorch, TensorFlow, or HuggingFace
• Experience deploying models in production (bonus if you've worked in a SaaS or healthtech environment)
• Comfort working with real-world, messy datasets and finding ways to make them useful
• Understanding of transformer architectures, CNNs, or diffusion models
• Bonus: experience with 3D modeling, STL files, or dental/medical imaging

• Be part of a company that's scaling with purpose—our AI directly supports better dental care
• A low-ego, high-execution team that values action over perfection
• Great benefits: health, dental and vacation
• You'll actually ship. Often.

Join us if you're ready to turn models into impact—not just experiments.

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