ai inference engineer

Qualcomm

<div><h2 class="jobSectionHeader">Company:
</h2>Qualcomm Middle East Information Technology Company LLC
<h2 class="jobSectionHeader">Job Area:
</h2>Engineering Group, Engineering Group &gt; Systems Engineering
General Summary:
About Us
As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all.
As a Qualcomm Datacenter AI Systems Engineer focused on AI Inference Platforms and Competitive Analysis, you will evaluate, benchmark, optimize, and validate end-to-end AI inference solutions built on Qualcomm AI accelerators. You will analyze real-world customer deployments, assess competitive platforms, and develop fact-based insights on performance, scalability, ecosystem readiness, and operational simplicity across modern GenAI workloads including LLMs, VLMs, RAG, and agentic applications.
About the role
In this role, you will work across hardware, software, frameworks, and deployment architectures to identify differentiators, gaps, and opportunities that shape Qualcomm's AI platform roadmap and customer engagement strategy. You will collaborate closely with architecture, product, systems, customer engineering, business development, and ecosystem teams to establish Qualcomm AI accelerators as a leading platform for enterprise AI inference deployments.
Minimum Qualifications:
<ul><li>Bachelor&rsquo;s degree in engineering, computer science, or Data Science with 8+ years of experience
</li><li>Master's degree in Engineering, Computer Science, Data Science or related field and 4+ year of related work experience.
</li></ul>OR
<ul><li>PhD in Engineering, Computer Science, or related field and 4+ years of Experience.
</li></ul>Required Skills:
<ul><li>Strong proficiency in Python and common AI/ML frameworks such as PyTorch, TensorFlow, or ONNX.
</li><li>Solid understanding of ML model development, deployment, and AI inference concepts for datacenter workloads.
</li><li>Strong foundation in system performance profiling, benchmarking, parallel computing, and workload analysis.
</li><li>Working knowledge of Linux-based development, containerized deployment, and cloud-native infrastructure concepts.
</li><li>Strong analytical, communication, and cross-functional collaboration skills with the ability to summarize technical findings clearly.
</li></ul>Preferred Qualifications:
<ul><li>Master's or PhD degree in Engineering, Computer Science, Information Systems, Electrical Engineering, Physics, or a related technical field with 4+ years of experience.
</li><li>Strong understanding of GenAI and enterprise inference workloads, including LLMs, VLMs/LVMs, embeddings, diffusion models, RAG pipelines, agents, and multi-turn application patterns.
</li><li>Hands-on experience evaluating AI accelerator platforms and deployment ecosystems, including Qualcomm AI inference accelerators and competitive platforms such as NVIDIA and AMD.
</li><li>Experience with production inference frameworks and serving stacks such as vLLM, Triton Inference Server, TensorRT-LLM, SGLang, KServe, Ray Serve, TorchServe, or equivalent model-serving technologies.
</li><li>Deep understanding of inference performance fundamentals, including prefill/decode behavior, KV-cache utilization, batching, scheduling, quantization, memory bandwidth, latency, throughput, TTFT, TPOT, and scaling efficiency.
</li><li>Experience designing and executing fact-based competitive benchmarks across single-node and multi-node deployments using representative customer workloads, model configurations, and deployment patterns.
</li><li>Knowledge of distributed inference architectures, including tensor parallelism, pipeline parallelism, expert parallelism/MoE serving, disaggregated serving, routing, autoscaling, and cluster-level orchestration.
</li><li>Experience with containerized and cloud-native deployment workflows using Docker, Kubernetes, Helm, GitOps, CI/CD, observability stacks, and infrastructure automation.
</li><li>Ability to assess ecosystem readiness, operational simplicity, tooling maturity, model onboarding complexity, reference architectures, observability, supportability, and deployment documentation quality.
</li><li>Background in compiler/runtime optimization, graph lowering, operator fusion, kernel optimization, or hardware-aware model optimization for ML workloads is a plus.
</li><li>Experience conducting technical competitive analysis across AI hardware platforms, frameworks, software ecosystems, deployment architectures, and customer workflows.
</li><li>Experience building customer-facing technical collateral, competitive positioning, deployment guides, benchmark reports, executive briefings, and gap analysis for AI infrastructure products.
</li><li>Strong cross-functional collaboration skills with demonstrated ability to work across product, architecture, engineering, customer engineering, business development, and ecosystem partner teams in a matrixed organization.
</li></ul>Principal Duties and Responsibilities will include:
<ul><li>End-to-end competitive analysis for Qualcomm AI inference accelerators across hardware, software, model-serving frameworks, deployment workflows, and customer-facing GenAI inference solutions.
</li><li>Evaluate real customer deployment patterns, including LLM/VLM serving, RAG, agentic workflows, embedding services, multi-model applications, and enterprise inference pipelines, to identify where Qualcomm leads, is at parity, or has gaps.
</li><li>Develop objective benchmark methodologies and execute performance studies covering latency, TTFT, TPOT, throughput, tokens/sec/user, concurrency, utilization, power efficiency, cost efficiency, and single-node and multi-node scaling.
</li><li>Compare Qualcomm solutions against competitive platforms such as NVIDIA and AMD across deployment maturity, model coverage, framework support, ecosystem readiness, operational simplicity, and total solution capability.
</li><li>Analyze inference architecture tradeoffs involving vLLM, Triton Inference Server, KServe, Ray Serve, Kubernetes, orchestration layers, KV-cache management, batching strategies, quantization, and distributed serving patterns.
</li><li>Drive model onboarding prioritization by studying competitive usage, customer demand, model popularity, framework adoption, and readiness gaps across open-source and enterprise GenAI workloads.
</li><li>Build and maintain reference architectures, deployment playbooks, sizing guidance, reproducible benchmark recipes, and customer-ready solution blueprints for Qualcomm AI inference platforms.
</li><li>Partner with architecture, compiler, runtime, framework, systems, and product teams to translate competitive findings into product requirements, roadmap priorities, optimization opportunities, and ecosystem investments.
</li><li>Perform system-level debugging and root-cause analysis across model, framework, runtime, driver, hardware, networking, memory, and orchestration layers to explain performance or deployment gaps.
</li><li>Create clear, fact-based competitive positioning, leadership readouts, technical reports, and customer-facing collateral that communicate differentiators, risks, gaps, and recommended actions.
</li><li>Collaborate with customer engineering, business development, sales, and partner teams to support customer evaluations, proof-of-concepts, competitive bakeoffs, and deployment activities.
</li><li>Stay current with the evolving AI inference ecosystem, including emerging models, serving frameworks, accelerator platforms, open-source projects, benchmarking practices, and enterprise deployment trends.
</li></ul>What's on Offer
Apart from working with great people, we offer the below:
<ul><li>Salary including housing &amp; transport allowance
</li><li>Stock (RSU's) and performance related bonus
</li><li>16 weeks fully paid Maternity Leave
</li><li>6 weeks fully paid Paternity Leave
</li><li>Employee stock purchase scheme
</li><li>Child Education Allowance
</li><li>Relocation and immigration support (if needed)
</li><li>Life and Medical Insurance
</li><li>Live+ Well Reimbursement for health and recreational membership fees
</li></ul>Minimum Qualifications:
<ul><li>Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Systems Engineering or related work experience.
</li></ul>OR
 
Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Systems Engineering or related work experience.
 
OR
 
PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.
*References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.
<div>Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here . Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).
</div>Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
To all Staffing and Recruiting Agencies : Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.
If you would like more information about this role, please contact Qualcomm Careers .</div>

Data Center AI Systems Engineer (Competitive Analysis) - Riyadh, KSA

DigitalOcean

<div><div>Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you'll find your place here. We value winning together&mdash;while learning, having fun, and making a profound difference for the dreamers and builders in the world.</div>
DigitalOcean is expanding its AI Infrastructure layer to support the next generation of AI-driven applications. We are seeking a Senior Engineer 2 to join our AI Inference Data Plane team. In this role, you will be a key technical leader responsible for designing, developing, and delivering high-scale, resilient data plane services that power our &quot;Inference as a Service&quot; offering. You will work at the intersection of distributed systems and specialized AI hardware to ensure our customers can deploy and scale their models with industry-leading performance and reliability. This is a hands-on role, requiring you to be able to develop high quality software while availing of all the productivity boosts granted by the latest AI coding agents.
<h2 class="jobSectionHeader">
What You'll Do:</h2>
<ul><li>Technical Leadership: Act as a technical leader on the team, driving the end-to-end design, development, and delivery of critical data plane components hosting large generative AI models.</li>
<li>System Design: Architect and refine system design proposals for our high-scale, multi-tenant AI inference cloud ecosystem, ensuring they meet rigorous availability and resiliency standards.</li>
<li>Performance Optimization: Implement and optimize distributed inference hosting using techniques like tensor/data parallelism, KV cache optimizations, and smart routing.</li>
<li>Collaboration: Work cross-functionally with Product Managers, customer-facing teams, and other engineering teams to align technical roadmaps with customer needs.</li>
<li>Distributed Serving at Scale: Build on Kubernetes-native distributed inference frameworks like llm-d (or alternatives such as NVIDIA Dynamo, Ray Serve, KServe) to deliver prefill/decode disaggregation, KV-cache-aware routing, tiered prefix caching, and wide expert parallelism for MoE models.</li>
<li>Flow Control &amp; Load Balancing: Solve the distributed-systems problems unique to LLM serving &mdash; inference-aware load balancing on queue depth, cache locality, and predicted latency; flow control and fairness across tenants; autoscaling inference pools; and moving gigabytes of KV-cache between prefill and decode instances with negligible overhead.</li>
<li>Open Source Contributions: Contribute upstream to llm-d, vLLM, and the inference gateway ecosystem, and represent DigitalOcean in these communities.</li>
<li>Mentorship: Coach and mentor junior engineers, fostering a culture of technical excellence and continuous improvement.</li>
<li>Operational Excellence: Maintain and operate critical, high-scale services, utilizing observability tools and defining SLOs to ensure superior platform health.</li>
</ul><h2 class="jobSectionHeader">What You'll Bring to DigitalOcean:</h2>
<ul><li>AI/ML Domain Knowledge: Hands-on experience hosting large language or multimodal models using inference engines like vLLM, SGLang, or TensorRT.</li>
<li>Inference Frameworks: Familiarity with distributed inference serving frameworks such as llm-d, NVIDIA Dynamo, or Ray Serve.</li>
<li>Inference Engine Depth: Hands-on experience with vLLM or alternatives (SGLang, TensorRT-LLM, TGI, Modular MAX), including internals like continuous batching, paged attention, and prefix caching.</li>
<li>Distributed Inference Fluency: Understanding of why cluster-scale serving is hard: KV-cache locality is partitioned across workers, naive round-robin routing destroys cache hit rates and tail latency, and disaggregated prefill/decode requires fast cross-pod KV transfer (e.g., NIXL).</li>
<li>Upstream Track Record: Merged contributions to vLLM, llm-d, SGLang, or similar projects strongly preferred.</li>
<li>Architecture Proficiency: Knowledge of common LLM architectures and optimization techniques (e.g., continuous batching, quantization).</li>
<li>Software Engineering: Expert-level proficiency in GoLang or Python and familiarity with gRPC.</li>
<li>Cloud Operations: Proven experience shipping customer-facing software products and running critical services in a high-scale environment similar to DigitalOcean.</li>
<li>Open Source Mindset: Experience integrating and building with open-source software.</li>
</ul><h2 class="jobSectionHeader">Compensation Range:</h2>
<ul><li>$139,200 - $174,000</li>
</ul>
<ul><li>This is a remote role</li></ul>

JR: 2026-7624

#LI-Remote<div><h2 class="jobSectionHeader">
Why You'll Like Working for DigitalOcean</h2>
<ul><li>We innovate with purpose. You'll be a part of a cutting-edge technology company with an upward trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As a member of the team, you will be a Shark who thinks big, bold, and scrappy, like an owner with a bias for action and a powerful sense of responsibility for customers, products, employees, and decisions.</li>
<li>We prioritize career development. At DO, you'll do the best work of your career. You will work with some of the smartest and most interesting people in the industry. We are a high-performance organization that will always challenge you to think big. Our organizational development team will provide you with resources to ensure you keep growing. We provide employees with reimbursement for relevant conferences, training, and education. All employees have access to LinkedIn Learning's 10,000+ courses to support their continued growth and development.</li>
<li>We care about your well-being. Regardless of your location, we will provide you with a competitive array of benefits to support you from our Employee Assistance Program to Local Employee Meetups to flexible time off policy, to name a few. While the philosophy around our benefits is the same worldwide, specific benefits may vary based on local regulations and preferences.</li>
<li>We reward our employees. The salary range for this position is based on market data, relevant years of experience, and skills. You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance. We also provide equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program.</li>
<li>DigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.</li>
</ul>
Application Limit: You may apply to a maximum of 3 positions within any 180-day period. This policy promotes better role-candidate matching and encourages thoughtful applications where your qualifications align most strongly.</div></div>

Senior Software Engineer I - AI Inference Data Plane

<div><div>Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you'll find your place here. We value winning together&mdash;while learning, having fun, and making a profound difference for the dreamers and builders in the world.</div>
DigitalOcean is expanding its AI Infrastructure layer to support the next generation of AI-driven applications. We are seeking a Senior Engineer 2 to join our AI Inference Data Plane team. In this role, you will be a key technical leader responsible for designing, developing, and delivering high-scale, resilient data plane services that power our &quot;Inference as a Service&quot; offering. You will work at the intersection of distributed systems and specialized AI hardware to ensure our customers can deploy and scale their models with industry-leading performance and reliability. This is a hands-on role, requiring you to be able to develop high quality software while availing of all the productivity boosts granted by the latest AI coding agents.
<h2 class="jobSectionHeader">
What You'll Do:</h2>
<ul><li>Technical Leadership: Act as a technical leader on the team, driving the end-to-end design, development, and delivery of critical data plane components hosting large generative AI models.</li>
<li>System Design: Architect and refine system design proposals for our high-scale, multi-tenant AI inference cloud ecosystem, ensuring they meet rigorous availability and resiliency standards.</li>
<li>Performance Optimization: Implement and optimize distributed inference hosting using techniques like tensor/data parallelism, KV cache optimizations, and smart routing.</li>
<li>Collaboration: Work cross-functionally with Product Managers, customer-facing teams, and other engineering teams to align technical roadmaps with customer needs.</li>
<li>Distributed Serving at Scale: Build on Kubernetes-native distributed inference frameworks like llm-d (or alternatives such as NVIDIA Dynamo, Ray Serve, KServe) to deliver prefill/decode disaggregation, KV-cache-aware routing, tiered prefix caching, and wide expert parallelism for MoE models.</li>
<li>Flow Control &amp; Load Balancing: Solve the distributed-systems problems unique to LLM serving &mdash; inference-aware load balancing on queue depth, cache locality, and predicted latency; flow control and fairness across tenants; autoscaling inference pools; and moving gigabytes of KV-cache between prefill and decode instances with negligible overhead.</li>
<li>Open Source Contributions: Contribute upstream to llm-d, vLLM, and the inference gateway ecosystem, and represent DigitalOcean in these communities.</li>
<li>Mentorship: Coach and mentor junior engineers, fostering a culture of technical excellence and continuous improvement.</li>
<li>Operational Excellence: Maintain and operate critical, high-scale services, utilizing observability tools and defining SLOs to ensure superior platform health.</li>
</ul><h2 class="jobSectionHeader">What You'll Bring to DigitalOcean:</h2>
<ul><li>AI/ML Domain Knowledge: Hands-on experience hosting large language or multimodal models using inference engines like vLLM, SGLang, or TensorRT.</li>
<li>Inference Frameworks: Familiarity with distributed inference serving frameworks such as llm-d, NVIDIA Dynamo, or Ray Serve.</li>
<li>Inference Engine Depth: Hands-on experience with vLLM or alternatives (SGLang, TensorRT-LLM, TGI, Modular MAX), including internals like continuous batching, paged attention, and prefix caching.</li>
<li>Distributed Inference Fluency: Understanding of why cluster-scale serving is hard: KV-cache locality is partitioned across workers, naive round-robin routing destroys cache hit rates and tail latency, and disaggregated prefill/decode requires fast cross-pod KV transfer (e.g., NIXL).</li>
<li>Upstream Track Record: Merged contributions to vLLM, llm-d, SGLang, or similar projects strongly preferred.</li>
<li>Architecture Proficiency: Knowledge of common LLM architectures and optimization techniques (e.g., continuous batching, quantization).</li>
<li>Software Engineering: Expert-level proficiency in GoLang or Python and familiarity with gRPC.</li>
<li>Cloud Operations: Proven experience shipping customer-facing software products and running critical services in a high-scale environment similar to DigitalOcean.</li>
<li>Open Source Mindset: Experience integrating and building with open-source software.</li>
</ul><h2 class="jobSectionHeader">Compensation Range:</h2>
<ul><li>$139,200 - $174,000</li>
</ul>
<ul><li>This is a hybrid role</li></ul>

JR: 2026-7624

#LI-Hybrid<div><h2 class="jobSectionHeader">
Why You'll Like Working for DigitalOcean</h2>
<ul><li>We innovate with purpose. You'll be a part of a cutting-edge technology company with an upward trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As a member of the team, you will be a Shark who thinks big, bold, and scrappy, like an owner with a bias for action and a powerful sense of responsibility for customers, products, employees, and decisions.</li>
<li>We prioritize career development. At DO, you'll do the best work of your career. You will work with some of the smartest and most interesting people in the industry. We are a high-performance organization that will always challenge you to think big. Our organizational development team will provide you with resources to ensure you keep growing. We provide employees with reimbursement for relevant conferences, training, and education. All employees have access to LinkedIn Learning's 10,000+ courses to support their continued growth and development.</li>
<li>We care about your well-being. Regardless of your location, we will provide you with a competitive array of benefits to support you from our Employee Assistance Program to Local Employee Meetups to flexible time off policy, to name a few. While the philosophy around our benefits is the same worldwide, specific benefits may vary based on local regulations and preferences.</li>
<li>We reward our employees. The salary range for this position is based on market data, relevant years of experience, and skills. You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance. We also provide equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program.</li>
<li>DigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.</li>
</ul>
Application Limit: You may apply to a maximum of 3 positions within any 180-day period. This policy promotes better role-candidate matching and encourages thoughtful applications where your qualifications align most strongly.</div></div>

Senior Engineer, Inference Data Plane

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

Cantina Labs

<div><div><h2 class="jobSectionHeader">Location</h2>
Remote (U.S. or Europe)
</div><div><h2 class="jobSectionHeader">Employment Type</h2>
Full time
</div><div><h2 class="jobSectionHeader">Department</h2>
Research</div></div>
 <div><div>About Cantina:
Cantina Labs is a social AI company, developing a suite of advanced real-time models that push the boundaries of expression, personality, and realism. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning.
If you're excited about the potential AI has to shape human creativity and social interactions, join us in building the future! 
<div></div>About the Role:
We are looking for an MLOps Engineer to build and scale the inference infrastructure for our generative audio models, including Text-to-Speech (TTS), voice conversion, and Automatic Speech Recognition (ASR). You will be responsible for designing and deploying high-performance systems that ensure low-latency, reliable, and scalable model serving for both streaming and batch inference. This role is central to bridging the gap between research and production, ensuring our audio models are optimized for performance and cost-efficiency as we scale.
What You&rsquo;ll Do:
<ul><li>Design and maintain inference infrastructure for generative audio model architectures.
</li><li>Implement and manage high-performance inference engines.
</li><li>Orchestrate service deployments using Kubernetes (K8S), implementing advanced autoscaling paradigms to handle varying traffic loads efficiently.
</li><li>Develop and automate robust CI/CD pipelines to streamline the testing and deployment of model artifacts and inference configurations.
</li><li>Monitor production systems, establishing observability practices to track latency, resource utilization, and overall model performance.
</li><li>Collaborate closely with research teams to optimize model serving paths and evaluate various inference strategies.
</li><li>Optimize inference performance for both streaming and batch applications.
</li></ul>What You&rsquo;ll Bring:
<ul><li>Deep understanding of modern audio model architectures (e.g., TTS, ASR) and their specific inference requirements.
</li><li>Strong hands-on experience with Kubernetes (K8S), container orchestration, and implementing autoscaling strategies for production workloads.
</li><li>Solid background in MLOps, including CI/CD automation and managing scalable cloud infrastructure.
</li><li>Proficiency in software engineering principles and experience with Python or Go for infrastructure tooling and backend services.
</li><li>Experience with GPU-accelerated inference and performance profiling techniques.
</li><li>Familiarity with high-performance inference engines (e.g., Triton Inference Server, vLLM-Omni) is a plus.
</li></ul>Compensation:
The anticipated annual base salary range for this role is between $125,000-$165,000 (&euro;110,000-&euro;145,000). When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data. 
<div></div>Benefits for U.S.-based roles:
<ul><li>Competitive salary and generous company equity
</li><li>Medical, dental, and vision insurance &ndash; 99.99% of premiums covered by Cantina
</li><li>42 days of paid time off, including:
<ul><li>15 PTO days
</li><li>10 sick days
</li><li>15 company holidays
</li><li>2 floating holidays
</li></ul></li><li>Generous parental leave &amp; fertility support
</li><li>401(k) retirement savings plan
</li><li>Lifestyle spending account &ndash; $500/month to use however you&rsquo;d like
</li><li>Complimentary lunch and snacks for in-office employees
</li><li>One Medical membership, and more!</li></ul></div></div>

Machine Learning Engineer, Ops

NVIDIA

<div>NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It&rsquo;s a unique legacy of innovation that&rsquo;s fueled by great technology&mdash;and amazing people. Today, we&rsquo;re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what&rsquo;s never been done before takes vision, innovation, and the world&rsquo;s best talent. As an NVIDIAN, you&rsquo;ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
Are you a software developer, deep learning engineer, or scientist excited to collaborate with developers? NVIDIA is looking for a Technical Marketing Engineer (TME) to communicate the latest advancements in our AI Platform Software to the developer and user community. The AI Platform Software team manages the complete high-performance AI software stack: core kernel and communication libraries such as cuDNN and NCCL, open training platforms like PyTorch, and Megatron, and accelerated inference platforms such as TensorRT-LLM and Dynamo. This role works closely with product, engineering, and marketing teams to develop key technical content that guides developers how to use NVIDIA's AI platforms. Examples include technical blog posts, user guides, walk-throughs, demonstrations, benchmarks, and more. We seek a candidate who has trained a model, optimized an inference server, or debugged a multi-GPU job&mdash;and who is eager to share their expertise with others. You will review the stack before release and often be the first outside the engineering team to fully understand it. You'll be part of a collaborative group of TMEs, product managers, and developer advocates committed to delivering the best AI software platform for our developer community.
What you'll be doing:<ul><li>
Investigating new training and inference features while assessing them from a developer's point of view. Writing blog posts, guides, and reference examples that developers can use.</li><li>
Collaborating with internal and external deep learning engineers and researchers to build product-based training material and how-to technical content.</li><li>
Being the champion for AI among NVIDIA developers by directly engaging with our developer community.</li><li>
Improving product documentation to be clear for developers and their agents.</li><li>
Growing the value of our software by bringing community and customer feedback back to our product and engineering teams.</li><li>
Providing guidance to deep learning developers by building code samples and proof of concept applications.</li><li>
Benchmarking and generating data for positioning NVIDIA's inference platforms as the lowest cost-per-watt.</li></ul>
What We Need to See:<ul><li>
Bachelor's degree in Computer Science, Computer Engineering, or similar field or equivalent experience.</li><li>
4+ years of practical experience in deep learning or machine learning, including research conducted during undergraduate and graduate studies.</li><li>
Hands-on experience with at least one training or inference framework such as PyTorch, JAX, Megatron, TensorRT-LLM, vLLM, SGLang, or comparable tools.</li><li>
Solid understanding of Python or C/C++, programming techniques, and software development.</li><li>
Something you have written or built for a technical audience that we can engage with: a blog post, tutorial, documentation set, conference talk, thesis chapter, or public repository.</li><li>
Passion for presenting to technical audiences and crafting content for developers.</li><li>
Prior success in balancing multiple projects at a time.</li></ul>
Ways to Stand Out from the Crowd:<ul><li>
Advanced knowledge of modern LLM and AI software architecture: attention kernels, parallelism strategies, quantization, KV cache management, and request scheduling.</li><li>
Sustained contributions to publicly accessible AI projects or developer forums.</li><li>
Experience running, tuning, or interpreting benchmarks on multi-GPU systems.</li><li>
Experience explaining a system you did not build to people who need to use it tomorrow.</li></ul>
Widely considered to be one of the technology world&rsquo;s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
#LI-Hybrid
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 136,000 USD - 212,750 USD for Level 3, and 160,000 USD - 253,000 USD for Level 4.<div><div><div><div><div><div><div>
You will also be eligible for equity and benefits.</div></div></div></div></div></div></div>
Applications for this job will be accepted at least until August 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.</div>

Technical Marketing Engineer - AI Platform Software

Lightning AI

<div><div><h2 class="jobSectionHeader">Who We Are</h2>

Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems&mdash;designed to take ideas from research to production with less friction.

Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.

We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.
<h2 class="jobSectionHeader">
The Way We Work</h2>

The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice:
<ul>
<li>Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping.</li>
<li>Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through.</li>
<li>Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together.</li>
<li>Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work.</li>
<li>Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most.</li>
<li>Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact.</li>
</ul>
</div><div></div><h2 class="jobSectionHeader">What We're Looking For</h2>

Lightning AI is looking to hire an AI Platform Support Engineer to join our US Customer Experience team, supporting ML engineers running large-scale training and inference workloads across cloud infrastructure, Kubernetes, and GPU platforms in production environments.

This role sits at the intersection of ML systems, cloud infrastructure, Kubernetes, and customers. You'll support engineers training models, deploying inference systems, and scaling GPU workloads in production.You are not a ticket router or traditional support engineer. You are a technical partner to ML teams - helping diagnose failures, improve reliability, and guide customers through complex distributed systems problems.

The problems range from Kubernetes scheduling and GPU orchestration to distributed PyTorch failures, inference latency, networking bottlenecks, storage performance, and platform reliability. You'll gain exposure to a wide variety of real world AI workloads across industries and help shape the infrastructure powering the next generation of ML applications.

This role is hybrid out of our Seattle, San Francisco, or New York office hubs, with an in-office requirement of at least 2 days per week and occasional team and company offsites. The role follows a Monday&ndash;Friday schedule, with working hours from 8:00 AM to 5:00 PM PST. We are not able to provide visa sponsorship for this role at this time.<h2 class="jobSectionHeader"> 

What You'll Do</h2>

Work Directly With ML Engineers
<ul>
<li>Partner directly with customer engineering teams running training and inference workloads in production</li>
<li>Help customers diagnose and resolve complex distributed systems and ML infrastructure issues</li>
<li>Act as a technical advisor during high impact incidents and platform degradation events</li>
<li>Translate infrastructure level issues into actionable guidance for ML engineers</li>
<li>Build credibility with customers through strong technical reasoning and clear communication</li>
</ul>
Debug ML Infrastructure &amp; Distributed Workloads
<ul>
<li>Investigate failures involving distributed training, Kubernetes orchestration, GPU allocation, networking, and storage systems</li>
<li>Troubleshoot PyTorch, CUDA, NCCL, and inference serving related issues</li>
<li>Analyze logs, metrics, traces, and system behavior to isolate root causes</li>
<li>Debug containerized workloads running across Kubernetes and bare metal GPU environments</li>
<li>Support customers scaling workloads across multi node GPU systems</li>
<li>Diagnose performance bottlenecks involving compute, memory, networking, or storage</li>
</ul>
Improve Reliability &amp; Platform Operations
<ul>
<li>Identify recurring patterns across customer issues and drive long term reliability improvements</li>
<li>Contribute to post incident reviews and operational improvements</li>
<li>Build internal tooling, automation, documentation, and runbooks</li>
<li>Partner closely with infrastructure, networking, and platform engineering teams</li>
<li>Help improve observability, operational visibility, and troubleshooting workflows</li>
<li>Improve the customer experience through better processes and technical guidance</li>
</ul>
What This Role Is Not

To set clear expectations:
<ul>
<li>This is not a traditional help desk or ticket routing support role</li>
<li>This is not purely customer success or account management</li>
<li>This is not a backend engineering role</li>
<li>This is not a passive escalation position</li>
</ul>
This role is for engineers who enjoy solving difficult technical problems while working closely with other engineers. 

<h2 class="jobSectionHeader">
What You'll Need</h2>
<h3 class="jobSectionHeader">Required Qualifications</h3>
<h4 class="jobSectionHeader">Infrastructure &amp; Systems</h4>
<ul><li>Strong software engineering and systems troubleshooting background</li>
<li>Experience with Kubernetes and containerized environments</li>
<li>Linux systems knowledge, including networking, storage, process management, and performance tuning</li>
<li>Experience with cloud infrastructure and distributed systems</li>
<li>Experience with observability and debugging tools such as Prometheus, Grafana, or OpenTelemetry</li>
</ul><h4 class="jobSectionHeader">ML Infrastructure Experience</h4>
<ul><li>Hands on experience operating machine learning workloads in production or research environments</li>
<li>Experience with distributed ML systems and tooling such as PyTorch, CUDA, or NCCL</li>
<li>Familiarity with GPU infrastructure and orchestration</li>
<li>Experience troubleshooting performance, reliability, or scaling issues in ML infrastructure</li>
<li>Understanding of the operational challenges involved in running ML systems at scale</li>
</ul><h4 class="jobSectionHeader">Collaboration</h4>
<ul><li>Strong communication skills and ability to work directly with highly technical customers and engineering teams</li>
<li>Comfortable operating in fast moving, highly ambiguous environments</li>
<li>Enjoys solving complex technical problems collaboratively</li>
</ul><h3 class="jobSectionHeader">Ideal Experience</h3>
<ul><li>Experience with large scale model training or distributed inference systems</li>
<li>Familiarity with Ray, Kubeflow, Slurm, or similar distributed scheduling platforms</li>
<li>Experience with InfiniBand, RDMA, or high-performance networking</li>
<li>Experience operating bare metal infrastructure</li>
<li>Familiarity with storage systems commonly used in ML environments</li>
<li>Experience working at an AI infrastructure, cloud, MLOps, or developer tooling company</li>
<li>Contributions to platform engineering, developer infrastructure, or operational tooling projects</li>
<li>Experience writing automation, tooling, or scripts in Python or similar languages</li>
</ul><div><h2 class="jobSectionHeader">Benefits and Perks</h2>

We offer a comprehensive and competitive benefits package designed to support our employees' health, well-being, and long-term success:
<ul>
<li>Comprehensive Health Coverage: Medical, dental, and vision coverage for employees and eligible dependents.</li>
<li>Meaningful Equity: RSUs that give employees a stake in the company's long-term success.</li>
<li>Retirement Savings: 401(k) matching (U.S.) and pension contributions (U.K.).</li>
<li>Flexible Time Off: Unlimited PTO, company holidays, and floating holidays to support work-life balance.</li>
<li>Company-Wide Winter Break: Two weeks of company closure each winter to disconnect and recharge.</li>
<li>Paid Parental &amp; Family Leave: Paid leave to support you and your family through life's important moments.</li>
<li>Professional Development: Annual learning and development allowance to support your professional growth.</li>
<li>Wellness Benefits: Wellness and work-from-home stipends to support your physical and mental well-being.</li>
<li>Sabbatical Program: Four weeks of paid sabbatical leave after four years of service.</li>
<li>Flexible Work: Flexible schedules and a hybrid work model for our office-based teams.</li>
<li>In-Office Meals: Complimentary meals at our office hubs.</li>
</ul>
Benefits may vary by location, team, and role. 


At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.</div></div>

AI Platform Support Engineer (US)

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