Senior Software Engineer (Computer Vision / ML Engineer)
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Full-time | Remote | Australia; Candidates must have full working rights in Australia.Full-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonMachine LearningPyTorchDeep LearningComputer Vision
Requirements
- Have deep knowledge of computer vision, machine learning, and deep learning.
- Have strong foundations in mathematics, statistics, optimization, or related disciplines.
- Demonstrate the ability to solve open-ended research and engineering problems.
- Have strong experience with Python and PyTorch or an equivalent modern deep-learning framework.
- Have experience designing controlled experiments, analyzing model behavior and failure modes, and evaluating models against challenging datasets.
- Be able to translate research concepts into efficient, reliable production systems.
- Have strong software engineering fundamentals and experience with C/C++, CUDA, TensorRT, and ONNX.
- Hold a bachelor's or master's degree in computer science, machine learning, mathematics, engineering, or a related discipline, or have equivalent demonstrated expertise.
- PhD or equivalent research experience in computer vision, machine learning, AI, mathematics, or a related field is highly desirable.
- Experience with biometrics, liveness or presentation attack detection, deepfakes, synthetic media, adversarial ML, or AI security is highly desirable.
- Familiarity with biometric security and presentation-attack standards such as ISO/IEC 30107 is highly desirable.
Responsibilities
- Investigate problems in computer vision, biometrics, liveness, anti-spoofing, deepfake detection, generative AI, and adversarial attacks.
- Form hypotheses, run controlled experiments, and analyze why approaches work or fail.
- Develop presentation, injection, deepfake, synthetic-media, and AI-generated attacks against systems.
- Build adversarial evaluations, expose system weaknesses, and develop robust defenses.
- Design, train, evaluate, and optimize deep-learning models for image, video, and multimodal data.
- Take research ideas from prototyping through benchmarking, optimization, deployment, and real-world evaluation.
- Reproduce and challenge emerging computer vision and AI research.
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