Efinix provides low-power, high-performance FPGA silicon platforms for edge computing and embedded acceleration. The company implements its proprietary Quantum fabric across FPGA product families…
Efinix provides low-power, high-performance FPGA silicon platforms for edge computing and embedded acceleration. The company implements its proprietary Quantum fabric across FPGA product families…
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Data Scientist
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Part-time • Hamburg, DE
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Full-time • Cambridge, GB
Mobile Developer
Full-timeMunich, DE
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AI Researcher
Part-timeNovi Sad, RS
Part-time • Novi Sad, RS
Technical Writer
Full-timeMunich, DE
Full-time • Munich, DE
Key Responsibilities
Design, develop, train, and evaluate machine learning and deep learning models, including CNNs, Transformers, Diffusion Models, and other Generative AI architectures.
Research, prototype, and implement state-of-the-art AI algorithms, architectures, and training methodologies.
Conduct experiments, benchmark performance, and optimize models for accuracy, efficiency, scalability, and deployment requirements.
Develop AI training, validation, testing, and inference pipelines.
Deploy and optimize AI solutions for embedded and edge computing environments.
Develop embedded software components to support AI inference and system integration on FPGA-based platforms.
Analyse and optimize end-to-end AI system performance, considering latency, throughput, memory utilization, and power efficiency.
Collaborate with cross-functional engineering teams to integrate AI technologies into products and applications.
Stay up to date with advancements in Artificial Intelligence, Generative AI, Embedded AI, and Edge Computing technologies.
Qualifications
Bachelor's, Master's, or Ph.D. degree in Computer Science, Artificial Intelligence, Electrical Engineering, Computer Engineering, or a related field.
Strong understanding of machine learning, deep learning, and neural network architectures.
Experience developing AI and deep learning models using PyTorch and/or TensorFlow.
Strong programming skills in Python.
Experience with C/C++ development.
Knowledge of model optimization techniques, including quantization, pruning, and model compression.
Experience designing and optimizing CNN architectures.
Experience with Generative AI models, including Transformers, Large Language Models (LLMs), Variational Autoencoders (VAEs), or Diffusion Models.
Familiarity with embedded software development and embedded Linux environments is an advantage.
Knowledge of FPGA-based AI deployment and acceleration techniques is an advantage.
Experience with FPGA development platforms or toolchains is an advantage.
Strong analytical, problem-solving, and debugging skills.
Ability to work effectively in a collaborative engineering environment.
