Algorithm Engineer - Deep Learning

Company Overview KLA is a global leader in diversified electronics for the semiconductor manufacturing ecosystem. Virtually every electronic device in the world is produced using our technologies. No laptop, smartphone, wearable device, voice-controlled gadget, flexible screen, VR device or smart car would have made it into your hands without us. KLA invents systems and solutions for the manufacturing of wafers and reticles, integrated circuits, packaging, printed circuit boards and flat panel displays.

The innovative ideas and devices that are advancing humanity all begin with inspiration, research and development. KLA focuses more than average on innovation and we invest 15% of sales back into R&D. Our expert teams of physicists, engineers, data scientists and problem-solvers work together with the world’s leading technology providers to accelerate the delivery of tomorrow’s electronic devices. Life here is exciting and our teams thrive on tackling really hard problems.

There is never a dull moment with us.

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.

Qualified candidates are expected to have a strong background and in-depth experience in deep learning, especially in object detection, segmentation, vision foundation models, and multimodal models. Candidates should also have a deep understanding of relevant theory and hands-on experience grounding DL/GenAI models in real application domains, with strong emphasis on performance, efficiency, and deployment.

The ideal candidate can work independently across the full deep learning project lifecycle, including conceptualizing, exploring, designing, implementing, optimizing, and deploying models.

• 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 and optimize DL model architectures, including new modules, efficient backbones, and model compression techniques (e.g., distillation).
• Optimize DL or GenAI model throughput and cost, including mixed-precision and low-precision inference and training (e.g., FP16, FP8).
• Work and communicate collaboratively with peers.
• Present ideas, concepts, and results in professional technical settings.

• Desired 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 developing and optimizing 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, with strong emphasis on efficiency, scalability, and deployment performance.
• Required experience with DL model optimization/distillation for mixed or reduced precision (e.g., FP16, FP8) to improve throughput, latency, and deployment efficiency.
• 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, Tensor Flow, 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.
• Experience in semiconductor process control is a plus.
• Minimum Qualifications Doctorate (academic) degree with 0 years of related work experience; or Master’s degree with 3 years of related work…