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# Algorithm Engineer - Deep Learning

- **Company:** Kla Corporation
- **Location:** Milpitas, CA, United States
- **Experience:** Experienced
- **Salary:** $136,300 - $231,700
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Computer Vision, C++ (Programming Language), Python (Programming Language), Natural Language Processing, Object Detection, Open Source Technology, Tensorflow, Rust (Programming Language), Pytorch, Large Language Models, Deep Learning, Generative AI, Guidewire PolicyCenter, Information Technology, Low Latency, Machine Learning Operations, Front End Software Development, Golang, Programming Languages
- **Published:** November 5, 2026
- **Apply:** https://www.careerbuilder.com/job-details/algorithm-engineer-deep-learning-milpitas-ca--92ac0524-a360-450f-8d66-7a830c78cce8

## About the Role

Algorithms, Deep Learning, Sales, Laptop PC, Flat Panel Displays, Ecosystems, Electronics, Semiconductor Manufacturing, Problem Solving Skills, Printed Circuit Board (PCB), 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., * 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, 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 in semiconductor process control is a plus., * 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.
 * Required experience with DL model optimization/distillation for mixed or reduced precision (e.g., FP16, FP8) to improve throughput, latency, and deployment efficiency.
 * Travel required: up to 10%.

## Description

The ideal candidate can work independently across the full deep learning project lifecycle, including conceptualizing, exploring, designing, implementing, optimizing, and deploying models. Responsibilities for this position include, but are not limited to:

 * 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.