Senior Machine Learning Research Scientist

Posted 12 days agoViewed
FinlandFull-TimeMachine Learning
Company:Axon
Location:Finland
Languages:English
Seniority level:Lead, +8 year
Experience:+8 year
Skills:
LeadershipPythonArtificial IntelligenceEmbedded SystemsKerasMachine LearningPyTorchTensorflowMentoring
Requirements:
PhD and with +8 year experience in Computer Science or a related field with a focus on MLLMs, computer vision, machine learning, or artificial intelligence. Proven track record of research excellence in machine learning, computer vision, robotics perception, demonstrated through publications in top-tier conferences or journals. Strong proficiency in programming languages such as Python, C/C++. Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras. Experience with ROS or robotic operational system. Drive one or more phases of the ML development lifecycle: shape datasets, investigate modeling approaches and architectures, train/evaluate/tune models and implement the end-to-end training pipeline. Leverage state-of-the-art research to deliver high quality models enabling multiple AI projects at scale. Contribute back to the research community via academic publications, tech blogs, open-source code and contributing to internal/external AI challenges. Experience in developing computer vision algorithms for resource-constrained devices is highly desirable. Excellent problem-solving skills, analytical thinking, and the ability to work independently as well as collaboratively in a team environment. Strong communication skills and the ability to effectively present complex technical concepts to both technical and non-technical audiences.
Responsibilities:
Convert and Ship CVML R&D ideas to Axon Products. Research and develop advanced MLLMs, GenAI, and Computer Vision techniques. Design and implement efficient and scalable MLLM models. Explore novel approaches to address challenges in object detection, recognition, tracking, segmentation, and scene understanding. Optimize algorithms for performance, memory footprint, and energy efficiency. Leverage hardware accelerators and optimize algorithms for specific hardware architectures. Evaluate the performance of MLLM models using real-world datasets. Stay up-to-date with the latest research trends in CVML, MLLMs, GenAI. Contribute to patent disclosures, academic publications, and technical documentation. Coach and mentor junior scientists.
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