Senior Yield Enhancement Engineer
Confirmed live in the last 24 hours
Cerebras Systems
Compensation
$175,000 to $250,000 annually
Job Description
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.
Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.
Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.
The Role: Senior Yield Enhancement Engineer
We are seeking a highly experienced Senior VLSI Product and Test Engineer with 7+ years of relevant experience in Semiconductor Testing/Failure Analysis/Yield Enhancement. The successful candidate will look at ATE datalogs, understand the defects in detail, disposition wafers based on ATE data and drive FA/Yield enhancement using physical/optical inspection techniques used in FA.
Suitable candidate will have depth in testing, characterization of silicon defects, failure modes, and experience delivering end-to-end solutions working closely with teams across chip design, fabrication, validation, production, and manufacturing.
Key Responsibilities
- Analyze ATE data logs, Shmoo plots, parametric characterization data, and spatial wafer defect patterns.
- Develop failure analysis tools using optical, photo emission, and laser-based defect localization techniques specific to Cerebras hardware.
- Develop and execute FIB (Focused Ion Beam) edit plans for Silicon root cause validation.
- Communicating with OSATs and Fab to drive production testing in HVM environment.
- Understand DFT strategies including hierarchical scan chains, distributed BIST, SRAM test methodologies, and perform diagnosis on ATE data.
- Collaborate closely with DFT engineers, silicon architects, designers, performance teams, and software engineers to enhance overall testability and yield
- Refine test programs across di/dt behavior, voltage-frequency characterization space, current limits, and thermal constraints based on ATE logs and disposition learnings.
- Understand and write Python scripts and UNIX environment.
Required Skills & Qualifications
- Bachelor's or Master's degree in Electrical Engineering / Computer Engineering, or related field
- 7+ years of hands-on experience in semiconductor test engineering/ FA/ Yield Enhancement.
- Hands-on experience with lab debug tools including Oscilloscopes (high-speed probing and signal integrity), wafer probe stations, probe cards, Keyence/Optical inspection systems, and advanced imaging techniques.
- Failure analysis (FA) expertise including use of optical probing tools, physical inspection workflows, and correlation of electrical failures to physical defects.
- Strong capability to read and understand Digital CMOS layouts, power grids, routing structures and SRAM arrays.
- ATE test program debugging, and yield improvement experience.
- Good interpersonal skills with the ability and desire to work as a standout colleague and problem solver.
- Proven track record of working cross-functionally, learning fast, and driving issues to closure
- Working knowledge of git repositories, GitHub, git actions/Jenkins, merge and release flows to streamline test and release
- Proficiency in programming languages: Python, C/C++, Perl for large-scale data analysis
Preferred Skills
- Develop fault isolation techniques using OBIRCH/IREM/LADA optical techniques.
- Experience with advanced test data analysis tools and machine learning techniques for yield optimization.
- Familiarity with advanced packaging technologies for wafer-scale systems (TSV, advanced interconnects).
- Familiarity with in-line testing and diagnostics using CPU memory and execution with self-checking.
- Knowledge of chip defect profiles and mitigation strategies across manufacturing steps.
Location
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