Senior Computer Vision Engineer
Posted 4ds ago
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Job Description
Senior Computer Vision Engineer developing deepfake and liveness detection models with a cross-functional R&D team in Hungary. Working on a large-scale facial and video database to ensure system integrity and authenticity.
Responsibilities:
- Build and improve deepfake and liveness detection models using CNNs, attention layers and vision-language models.
- Design, train and evaluate models end to end, from preparing data to checking results, following good engineering practices.
- Fine-tune vision-language models using efficient methods such as LoRA.
- Make models smaller and faster for both cloud and on-device / mobile use.
- Build and maintain training and deployment pipelines to get models into production on AWS (e.g. EC2, Athena).
- Use AI-assisted coding tools such as Claude Code to speed up experimentation and prototyping.
- Share progress clearly with technical and non-technical colleagues, and own independent research & development initiatives.
Requirements:
- A BSc, MSc or Ph.D. in engineering, computer science or a related field.
- At least 5 years of professional experience developing and deploying deep learning and computer vision models.
- Strong grasp of the end-to-end ML workflow: preparing data, training models and evaluating results.
- Solid grounding in modern neural network architectures, including convolutional networks and attention mechanisms.
- Working understanding of large language models (LLMs), vision-language models (VLMs) and adaptation techniques.
- Experience working in agile environments, with strong analytical and problem-solving skills.
- Experience with deepfake detection, face anti-spoofing / liveness, biometric security or media forensics is a strong plus.
- Experience leading or mentoring machine learning engineers is a strong plus.
- Expert-level Python, with proven experience building deep learning models in production.
- Familiarity with C++ is a plus.
- Professional experience with modern deep learning frameworks such as PyTorch, TensorFlow or JAX.
- Hands-on experience deploying and optimising models on AWS (e.g. EC2, Athena) and related MLOps services. (MLFlow etc)
- Experience optimising neural networks for deployment, including model compression, quantisation and runtime optimisation.
Benefits:
- Unlimited paid holidays
- Life insurance and 100% paid sick leave
- Working from home setup
- Innovative employee recognition and reward system, Bonusly
- Strong benefit package including subsidized gym membership / sport allowance, online and offline team-building events, flexible working hours
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