About Centific
Centific is a frontier AI data foundry that curates diverse, high-quality data, using our purpose-built technology platforms to empower the Magnificent Seven and our enterprise clients with safe, scalable AI deployment. Our team includes more than 150 PhDs and data scientists, along with more than 4,000 AI practitioners and engineers. We harness the power of an integrated solution ecosystem—comprising industry-leading partnerships and 1.8 million vertical domain experts in more than 230 markets—to create contextual, multilingual, pre-trained datasets; fine-tuned, industry-specific LLMs; and RAG pipelines supported by vector databases. Our zero-distance innovation™ solutions for GenAI can reduce GenAI costs by up to 80% and bring solutions to market 50% faster.
Our mission is to bridge the gap between AI creators and industry leaders by bringing best practices in GenAI to unicorn innovators and enterprise customers. We aim to help these organizations unlock significant business value by deploying GenAI at scale, helping to ensure they stay at the forefront of technological advancement and maintain a competitive edge in their respective markets.
About Job
About the RoleWe are looking for a passionate and curious Junior Vision AI Engineer to join our growing AI team. In this role, you will work on developing, training, and deploying computer vision models that power real-world intelligent applications. You'll collaborate closely with senior engineers, data scientists, and product teams to build scalable vision AI solutions.
Key Responsibilities- Develop and fine-tune computer vision models for tasks such as image classification, object detection, segmentation, and tracking
- Assist in data collection, annotation, preprocessing, and augmentation pipelines
- Train and evaluate deep learning models using frameworks like PyTorch or TensorFlow
- Support deployment of vision models to production environments (cloud, edge, or embedded)
- Write clean, well-documented, and testable code
- Participate in code reviews, model evaluations, and performance benchmarking
- Collaborate with cross-functional teams to understand requirements and translate them into technical solutions
- Stay current with the latest research in computer vision and contribute ideas for innovation
- Bachelor's or Master's degree in Computer Science, Electrical Engineering, AI/ML, or a related field
- Foundational knowledge of deep learning and computer vision concepts (CNNs, transformers, etc.)
- Hands-on experience (academic or professional) with PyTorch or TensorFlow
- Familiarity with vision libraries such as OpenCV, torchvision, or Hugging Face
- Basic proficiency in Python and standard ML tooling (NumPy, Pandas, scikit-learn)
- Understanding of model training, evaluation metrics (mAP, IoU, accuracy), and debugging
- Exposure to datasets like ImageNet, COCO, or similar
- Experience with video understanding, optical flow, or temporal modeling
- Familiarity with model optimization techniques (quantization, pruning, ONNX export)
- Exposure to edge deployment (NVIDIA Jetson, TensorRT, ONNX Runtime)
- Knowledge of MLOps tools (MLflow, DVC, Weights & Biases)
- Contributions to open-source projects or published research
- Experience with annotation tools (CVAT, Label Studio, Roboflow)
Skills Required
- Bachelor's or Master's degree in Computer Science, Electrical Engineering, AI/ML, or related field
- Foundational knowledge of deep learning and computer vision concepts (CNNs, transformers)
- Hands-on experience (academic or professional) with PyTorch or TensorFlow
- Familiarity with vision libraries such as OpenCV, torchvision, or Hugging Face
- Basic proficiency in Python and standard ML tooling (NumPy, Pandas, scikit-learn)
- Understanding of model training, evaluation metrics (mAP, IoU, accuracy), and debugging
- Exposure to datasets like ImageNet, COCO, or similar
- Experience with video understanding, optical flow, or temporal modeling
- Familiarity with model optimization techniques (quantization, pruning, ONNX export)
- Exposure to edge deployment (NVIDIA Jetson, TensorRT, ONNX Runtime)
- Knowledge of MLOps tools (MLflow, DVC, Weights & Biases)
- Contributions to open-source projects or published research
- Experience with annotation tools (CVAT, Label Studio, Roboflow)
Centific Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Centific and has not been reviewed or approved by Centific.
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Healthcare Strength — Feedback suggests core medical, dental, and vision coverage is comprehensive, with mental‑health support included. U.S. offerings also include HSA/FSA and are described as solid insurance options.
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Flexible Benefits — Feedback suggests flexible hours and work‑from‑anywhere options are widely promoted, with flexible PTO available in some contexts. Learning stipends and development programs add adaptable elements to the package.
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Parental & Family Support — Feedback suggests paid parental leave is explicitly available to all parents. This positions family leave as an accessible component of the overall package.
Centific Insights
What We Do
Zero distance innovation for GenAI creators and industries Expertly engineering platforms and curating multimodal, multilingual data, we empower the ‘Magnificent Seven’ and enterprise clients with safe, scalable AI deployment We a team of over 150 PhDs and data scientists, along with more than 4,000 AI practitioners and engineers. We bring platforms, partners and 1.8 million vertical domain experts to create high-quality pre-trained datasets, fine-tuned industry-specific LLMs, and RAG pipelines supported by vector databases. These innovations can reduce GenAI costs by up to 80% and bring GenAI solutions to market 50% faster in 230 locales.
