Machine Learning Engineer (NCG 2026)

Astera Labs (NASDAQ: ALAB) provides rack-scale AI infrastructure through purpose-built connectivity solutions. By collaborating with hyperscalers and ecosystem partners, Astera Labs enables organizations to unlock the full potential of modern AI. Astera Labs’ Intelligent Connectivity Platform integrates CXL®, Ethernet, NVLink, PCIe®, and UALink™ semiconductor-based technologies with the company’s COSMOS software suite to unify diverse components into cohesive, flexible systems that deliver end-to-end scale-up, and scale-out connectivity. The company’s custom connectivity solutions business complements its standards-based portfolio, enabling customers to deploy tailored architectures to meet their unique infrastructure requirements. Discover more at www.asteralabs.com . AI/ML Engineer - Silicon Development Automation Position Overview We are seeking an AI/ML Engineer to design and implement AI-enabled workflows that accelerate silicon development processes across the chip design lifecycle. You will apply deep learning and generative AI techniques to optimize EDA (Electronic Design Automation) workflows, spanning frontend design through backend physical design and Design-for-Test (DFT) implementation. Key Responsibilities Workflow Development & Optimization - Develop AI-enabled automation solutions for frontend and backend silicon development domains including circuit design, design verification, formal verification, static code analysis, debugging, or for backend physical design workflows. - Contribute to agentic workflows that coordinate silicon development tasks AI/ML Model Development - Implement Generative AI systems using Context Engineering, Retrieval-Augmented Generation (RAG) and Agentic techniques to integrate domain-specific EDA tooling with LLM capabilities - Apply context engineering techniques to encode chip design constraints and specifications into model inputs - Participate in building evaluation suites and internally relevant benchmark data for silicon development AI applications Technical Implementation - Write production Python and C++ code for AI inference and training pipelines - Build data pipelines for processing design databases, netlists, and verification artifacts - Develop evaluation frameworks with domain-specific metrics for design quality and convergence Required Qualifications Education & Background - Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field - Documented evidence of deep learning, generative AI, or chip design/verification expertise through one of: published papers, thesis work, GitHub repositories, or research projects Technical Proficiency - Strong Python programming for ML development and data processing - Solid C++ programming skills - Familiarity with optimization algorithms and their applications - Hands-on work with RAG systems or agentic AI workflows Domain Knowledge - Working knowledge of VLSI design flows, chip design, or verification methodologies - Understanding of EDA tools and design automation concepts Preferred Qualifications - Hands-on experience with PyTorch and deep learning frameworks - Contributions to open-source EDA tools or design automation projects - Prior coursework or projects in EDA or silicon development related activities - Experience with model evaluation frameworks and AI development best practices Technical Competencies - Python ML Development - C++ Programming - VLSI/Silicon Design Fundamentals - Generative AI Applications - RAG & Context Engineering Basics - Optimization Algorithm Implementation The base salary range for this role is between $140,000 - $160,000 We know that creativity and innovation happen more often when teams include diverse ideas, backgrounds, and experiences, and we actively encourage everyone with relevant experience to apply, including people of color, LGBTQ+ and non-binary people, veterans, parents, and individuals with disabilities.

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