Machine Learning & Computer Vision Engineer
Spain - Barcelona
Nivoda: Where Innovation and Gemstones Meet
At Nivoda, we are passionate about empowering jewelry retailers and gemstone suppliers to thrive in today's dynamic market. As the leading B2B diamond and gemstone marketplace, we are dedicated to providing an exceptional platform that connects jewellery businesses of all sizes with the global diamond supply.
Our team of over 500 dedicated employees, many with a wealth of industry experience, have meticulously developed our award-winning platform that addresses the unique challenges of the jewellery sector. With Nivoda, you can buy and sell diamonds securely, efficiently, hassle-free, and at the most competitive prices.
About the role
Our Intelligent Digital Quality (IDQ) pipeline is a cornerstone of that vision: it automatically inspects and enhances hundreds of thousands of product images and videos every day so buyers can evaluate stones with confidence.
High‑quality visual media is critical for trust in the jewelry trade. We’re looking for a hands‑on engineer who can push the boundaries of computer vision and graphics to assess, score and improve the visual fidelity of every asset that flows through Nivoda’s platform – at global scale and realtime speed.
What You’ll Do
Design & build new algorithms for image‐quality assessment, enhancement and restoration (blur detection, color normalization, glare removal, super‑resolution, etc.).
Develop & train deep‑learning models (PyTorch) using large, diverse datasets; own the full lifecycle from data curation to production rollout.
Integrate models into production services with robust, well‑tested Python/C++ code, Docker/Kubernetes packaging and AWS or GCP deployment.
Define objective quality metrics and automated test suites to continuously measure media quality across the marketplace.
Collaborate cross‑functionally with product, data engineering, design and QA teams to align on quality standards and user impact.
Stay ahead of the curve by researching and prototyping emerging techniques (e.g., diffusion models, NeRFs, differentiable rendering).
Minimum Qualifications
Master’s degree in Computer Science, Electrical Engineering, Applied Math or related field.
3 + years of professional experience delivering production‑grade computer‑vision or graphics systems.
Deep understanding of image‑processing fundamentals (filtering, transforms, color spaces, camera models) and modern deep‑learning architectures (CNNs, Vision Transformers).
Strong software‑engineering skills: Python and / or C++, Git, CI/CD, automated testing.
Familiarity with cloud‑native ML workflows (containers, REST / gRPC APIs, MLOps, model monitoring).
Familiarity with Postgres/SQL
Preferred Qualifications
PhD or equivalent research experience in vision, graphics or computational photography.
Experience with generative models (GANs, diffusion) for super‑resolution, deblurring or inpainting.
Exposure to 3D graphics / rendering pipelines (OpenGL, Vulkan, ray tracing, differentiable renderers).
Experience working with large‑scale media pipelines (tens of millions of images / videos) and streaming data.
Experience with OpenCV or similar
What we offer:
Dynamic working environment in a rapidly growing company.
Enjoy a pleasant, low-hierarchy work environment.
Engage in intellectually challenging work that contributes significantly to Nivoda’s success and scalability.
Flexible working hours and a vibrant company culture.
Plenty of opportunities for growth and learning.
Unlimited holiday allowance.
Chance to join and contribute to a company during its exponential expansion phase.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: 3D graphics APIs Architecture AWS CI/CD Computer Science Computer Vision Diffusion models Docker Engineering GANs GCP Generative modeling Git Kubernetes Machine Learning Mathematics MLOps NeRFs OpenCV PhD Pipelines PostgreSQL Prototyping Python PyTorch Research SQL Streaming Testing Transformers Vulkan
Perks/benefits: Career development Flex hours Startup environment
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