Qureos Mentor

Cerebras Systems

<div><div><h2 class="jobSectionHeader">Location</h2>
Headquarters/Sunnyvale Office
</div><div><h2 class="jobSectionHeader">Employment Type</h2>
Full time
</div><div><h2 class="jobSectionHeader">Location Type</h2>
Hybrid
</div><div><h2 class="jobSectionHeader">Department</h2>
Hardware Departments</div></div>
 <div><div>Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; 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.
 
 
Cerebras works with the leading 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.
Cerebras Systems Inc. has multiple openings for Hardware Analytics Engineer
Title: Hardware Analytics Engineer
Job Duties:
<ul><li>Design and optimize scalable data pipeline architectures for multi-terabyte hardware telemetry, reliability analytics, and performance optimization.
</li><li>Architect, develop, and optimize hyperscale data pipeline frameworks and ETL processes to aggregate, process, and analyze multi-terabyte hardware performance and telemetry streams, including utilization, power, thermal, acoustic, and reliability metrics across heterogeneous compute, storage, and AI server platforms, ensuring hardware performance compliance and operational reliability.
</li><li>Design and implement hardware performance analysis and anomaly detection systems using Python, SQL, Tableau, Hive, and Spark to forecast hardware failure curves, identify performance bottlenecks, and generate prescriptive recommendations for hardware and system optimization.
</li><li>Lead hardware characterization experiments and thermal/cooling A/B studies to evaluate operational envelopes, delivering validated strategies that reduce carbon footprint, improve water usage efficiency, and maintain or enhance system reliability.
</li><li>Engineer telemetry ingestion, monitoring, and visualization systems to provide real-time, high-fidelity hardware health data to hardware, firmware, and datacenter operations teams, enabling data-driven decision-making at scale.
</li><li>Define, operationalize, and maintain custom efficiency and reliability metrics; perform root cause analysis of systemic failures using large-scale statistical and machine learning methods; and deploy solutions that improve platform scalability, energy efficiency, and sustainability.
</li><li>Collaborate with cross-functional engineering teams to troubleshoot complex failures, isolate defective components, and implement systemic fixes across CPU, GPU, DRAM, PCIe, networking, and storage subsystems.
</li><li>Support the evolution and optimization of next-generation AI platforms and silicon products, including hardware subsystems (CPU, GPU, DRAM, PCIe, networking, and storage), to meet the performance, scalability, and efficiency demands of large language model training and inference workloads.
</li></ul>Minimum Requirements:
Master&rsquo;s degree or foreign equivalent degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field and 3 years of experience as Hardware Analytics Engineer, Hardware Engineer, Data Engineer, or a related occupation required.
Required Skills:
<ul><li>Large-scale data pipeline architecture and ETL, distributed data processing (Hive, Spark), and dashboard development;
</li><li>Python, SQL, Tableau, Linux, and automation scripting;
</li><li>Design, training, and deployment of machine learning models for hardware performance optimization and failure prediction;
</li><li>Predictive modeling, statistical analysis, A/B testing, anomaly detection, and data visualization in hardware reliability and performance; and
</li><li>Hardware analytics for compute, storage, and AI servers; power and thermal optimization; GPU burn-in efficiency optimization; and reliability modeling for AI hardware systems and components including CPU, GPU, DRAM, and SSD. 
</li></ul><div></div>Additional Information:
Employer&rsquo;s name: Cerebras Systems Inc.
Job site : 1237 E Arques Avenue, Sunnyvale, CA 94085
Telecommuting permitted
Salary Range: $213,675.00 per year to $225,000.00 per year
If you are interested in applying for this position, please apply online on this web page or mail resume to HR at Cerebras Systems Inc., 1237 E Arques Avenue, Sunnyvale, CA 94085. Please reference Job # 144 on resume or cover letter.
Why Join Cerebras
People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we&rsquo;ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:
<div><ul><li>Build a breakthrough AI platform beyond the constraints of the GPU.
</li></ul><ul><li>Publish and open source their cutting-edge AI research.
</li></ul><ul><li>Work on one of the fastest AI supercomputers in the world.
</li></ul><ul><li>Enjoy job stability with startup vitality.
</li></ul><ul><li>Our simple, non-corporate work culture that respects individual beliefs.</li></ul></div> 

Apply today and become part of the forefront of groundbreaking advancements in AI!
Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.</div></div>

Hardware Analytics Engineer

<div><div><h2 class="jobSectionHeader">Location</h2>
UAE
</div><div><h2 class="jobSectionHeader">Employment Type</h2>
Full time
</div><div><h2 class="jobSectionHeader">Department</h2>
Software</div></div>
 <div><div>Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; 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.
 
 
Cerebras works with the leading 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.
About The Role
As an Applied AI Scientist in the FieldML team, you will be responsible for developing and customizing large language models and more broadly large-scale deep learning models to solve specific customer problems. You won't just advise; you will build. You will bridge the gap between state-of-the-art research and real-world applications by helping customers harness the power of the Cerebras Wafer-Scale Engine (WSE) for their AI initiatives.
We are looking for experienced AI Scientists who are passionate about the &quot;applied&quot; side of machine learning - those who enjoy not just reading papers, but implementing, training, and scaling models to solve complex business and scientific problems. You will work on a diverse range of projects, from training bespoke models from scratch to fine-tuning and optimizing the latest Large Language Models (LLMs) for specific industry verticals, to designing and building components for custom agentic systems.
The ideal candidate has experience in large model training and/or post-training, a deep understanding of training dynamics and model convergence, and expertise in data curation, combined with strong communication skills.
Key Responsibilities
<ul><li>Customer Use Case Discovery &amp; Project Scoping
<ul><li>Collaborate with customer stakeholders to identify the best approaches to their business problem with AI.
</li><li>Contribute to the technical scoping of engagements, including feasibility analysis, data quality/availability/readiness assessments, and the selection of optimal model architectures.
</li><li>Define project milestones, success metrics, and rigorous evaluation benchmarks to ensure the solution delivers measurable value to the customer&rsquo;s business.
</li></ul></li><li>Custom SOTA Models and AI Systems Development
<ul><li>Architect and execute end-to-end training recipes for custom models, tailoring model architecture and training recipes to meet customer-specific performance and accuracy requirements.
</li><li>Design and implement sophisticated adaptation strategies, including continuous pre-training on private datasets, supervised fine-tuning (SFT), and post-training alignment via RLHF or DPO.
</li><li>Take full ownership of the training pipeline, from high-performance data preprocessing and tokenization to hyperparameter tuning and loss-curve analysis.
</li><li>Navigate the nuances of model convergence on specialized hardware, performing deep-dive analysis into loss dynamics and gradient stability.
</li><li>Scale training workloads across Cerebras clusters, ensuring efficient utilization of the hardware for multi-billion parameter models.
</li><li>Build and optimize the core components of agentic systems, focusing on tool-use capabilities, long-context reasoning, and multi-step planning.
</li></ul></li><li>Technical Customer Leadership
<ul><li>Serve as an AI/ML subject matter expert during technical deep-dives, translating customer requirements into precise training recipes.
</li><li>Build and maintain strong customer relationships to become their go-to AI/ML expert.
</li></ul></li><li>Internal Research and Engineering Collaboration
<ul><li>Act as the &quot;voice of the customer&quot; for internal R&amp;D and engineering teams to drive improvements in our software stack and hardware utilization.
</li><li>Partner with internal ML teams and product teams on prioritization of novel model architectures with Cerebras software stack, development of training recipes and internal case studies.
</li><li>Distill customer-facing successful projects into internal playbooks, helping scale the FieldML team&rsquo;s ability to deliver specialized models.
</li></ul></li></ul>Skills And Qualifications
<ul><li>Education: Master&rsquo;s or PhD in Computer Science, Machine Learning, or related fields.
</li><li>Broad Deep Learning Expertise: Expert-level understanding of modern model architectures, including dense transformers, MoEs, multimodal and sequence models, scaling laws and training dynamics.
</li><li>Hands-on Trainig Experience: Proven track record of training and/or fine-tuning large models (1B+ parameters) and direct experience with the challenges of large-scale model training.
</li><li>Engineering Proficiency: Mastery of Python and PyTorch, experience with distributed training frameworks and large-scale distributed data processing pipelines and tools.
</li><li>Strong Interpersonal and Communication Skills: Effective in collaborative and fast-paced team settings, able to work autonomously and within a team in a dynamic environment, managing multiple projects and pivoting as customer needs evolve. Able to present complex technical results to diverse audience - from C-level executives to research scientists, and to work collaboratively to solve customers&rsquo; unique challenges.
</li></ul>Why Join Cerebras
People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we&rsquo;ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:
<div><ul><li>Build a breakthrough AI platform beyond the constraints of the GPU.
</li></ul><ul><li>Publish and open source their cutting-edge AI research.
</li></ul><ul><li>Work on one of the fastest AI supercomputers in the world.
</li></ul><ul><li>Enjoy job stability with startup vitality.
</li></ul><ul><li>Our simple, non-corporate work culture that respects individual beliefs.</li></ul></div> 

Apply today and become part of the forefront of groundbreaking advancements in AI!
Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.</div></div>

Applied AI/ML Scientist

Join Cerebras Systems and explore 2   exciting career opportunities. They're hiring for various roles with options for remote or on-site work. Apply today and become part of their innovative team.

Cerebras Systems

About Company

Hardware Analytics Engineer

Applied AI/ML Scientist