About the job
We are looking for a full-time Deep Learning Algorithm Engineer who is passionate about pioneering Deep Learning (DL), foundation models, and GenAI for image processing and computer vision applications in the semiconductor process control business.
Responsibilities
Understand state-of-the-art (SOTA) deep learning and GenAI models.
Connect SOTA DL modeling approaches to domain problem statements.
Analyze modeling requirements based on product feature requirements.
Design deep learning and GenAI models to meet modeling requirements.
Implement modeling prototypes and perform analysis.
Perform model training and/or tuning on domain datasets.
Evaluate and validate model performance against defined metrics.
Analyze model performance bottlenecks.
Design new DL modules or components to improve performance.
Optimize DL or GenAI model throughput and/or cost.
Perform A/B testing between models on real data.
Work and communicate collaboratively with peers.
Present ideas, concepts, and results in professional technical settings.
Qualifications
Minimum
Doctorate (academic) degree with 0 years of related work experience; or Master’s degree with 3 years of related work experience.
Academic or industrial experience applying deep learning or GenAI to real-world problem(s), with impactful results.
Demonstrated deep learning expertise via technical publications in top conferences (e.g., NeurIPS, CVPR, ICML, ICLR, KDD, SIGGRAPH, etc.) and/or industrial patents and/or impactful open-source projects is required.
Travel required: up to 10%.
Preferred
Ph.D. in Electrical Engineering, Computer Science, or related quantitative fields.
Academic or industrial experience applying deep learning or GenAI to real-world problem(s), with impactful results.
In-depth experience applying deep learning, Vision Foundation Models (VFM), or Vision Language Models (VLM) in at least one of the following areas: computer vision, image processing, robotics, NLP, or equivalent.
Experience with GenAI coding tools, vibe coding, or vibe engineering.
Proficiency in Python and one additional programming language from: C++, Java, Rust, Go.
Proficiency in at least one deep learning framework (e.g., PyTorch, TensorFlow, JAX, or equivalent).
Demonstrated deep learning expertise via technical publications in top conferences (e.g., NeurIPS, CVPR, ICML, ICLR, KDD, SIGGRAPH, etc.) and/or industrial patents and/or impactful open-source projects is required.
Travel required: up to 10%.
Experience with DL model optimization for reduced precision (e.g., FP8, FP4) is a plus.
Experience with VFM pretraining or post-training is a plus.
Experience in semiconductor process control is a plus.