ai inference engineer

Exxeta

<div><div><div><div><div><div><p>Frankfurt am Main, Karlsruhe, Leipzig, Mannheim, Stuttgart</p>
</div></div><div><p><b>Als AI Inference Engineer baust du die technische Grundlage f&uuml;r produktive AI-Systeme in regulierten Umgebungen. Du entwickelst und betreibst LLM-Inferenzplattformen, die on-premises oder in privaten Cloud-Umgebungen laufen &ndash; sicher, skalierbar, beobachtbar und wirtschaftlich.</b></p>
<p><b>Du sorgst daf&uuml;r, dass moderne Modelle nicht nur in einer Demo &uuml;berzeugen, sondern unter realen Produktionsbedingungen zuverl&auml;ssig funktionieren: mit sauberer GPU-Planung, niedriger Latenz, kontrollierten Kosten, belastbarem Monitoring und klar definierten Betriebsmodellen.</b></p>
</div><h3 class="jobSectionHeader"><b>Was erwartet dich</b></h3>
<div><div><ul><li>Du konzipierst, entwickelst und betreibst produktive LLM-Inferenzplattformen f&uuml;r Kunden mit hohen Anforderungen an Datensouver&auml;nit&auml;t, Sicherheit und Betriebskontrolle &ndash; on-premises, in privaten Cloud-Umgebungen oder souver&auml;nen europ&auml;ischen Cloud-Setups.</li>
<li>Gemeinsam mit Cloud-, Plattform-, Security- und Data-Engineering-Teams sowie unseren Kunden &uuml;berf&uuml;hrst du AI-Use-Cases in den produktiven Betrieb.</li>
<li>Dabei integrierst du moderne Inferenz-Engines und Open-Weights-Modelle in Kubernetes-, Container- und Plattformumgebungen.</li>
<li>Au&szlig;erdem planst und optimierst du GPU- und Speicherressourcen sowie Inferenz-Workloads: Von Modellgr&ouml;&szlig;en, Quantisierung und Batching bis hin zu KV-Cache-Strategien, Latenz, Durchsatz und Kosten.</li>
<li>Du verantwortest die Runtime produktiver AI-Systeme, inklusive Modellserving, APIs, Authentifizierung, Secrets, Observability, Logging</li>
<li>Aus Kundenprojekten entwickelst du wiederverwendbare Referenzarchitekturen, Deployment-Templates und Betriebs-Playbooks und st&auml;rkst so unsere Applied-AI-Capability.</li>
</ul></div></div><h3 class="jobSectionHeader"><b>Was erwarten wir von dir</b></h3>
<div><div><ul><li><b>Pers&ouml;nlicher Background:</b> Erfahrung in Platform Engineering, Cloud Infrastructure, MLOps, LLMOps, DevOps, Backend Engineering oder Machine Learning Engineering. Entscheidend ist deine Erfahrung im Aufbau und Betrieb produktiver Systeme und dein Antrieb zu schneller pers&ouml;nlicher Weiterentwicklung</li>
<li><b>Inference Engineering:</b> Du verstehst die technischen und wirtschaftlichen Zusammenh&auml;nge moderner LLM-Inferenz, von Model-Serving und GPU-Auslastung &uuml;ber Quantisierung, Batching und KV-Cache-Management bis hin zu Latenz, Durchsatz und Kosten.</li>
<li><b>Cloud &amp; Plattformen:</b> Docker, Kubernetes, Helm, Terraform, CI/CD, Linux sowie Observability geh&ouml;ren f&uuml;r dich zum Arbeitsalltag.</li>
<li><b>AI-Verst&auml;ndnis: </b>Du kannst Transformer-basierte Modelle wie LLMs und Embeddings einordnen und fundierte technische Entscheidungen f&uuml;r produktive AI-Systeme treffen.</li>
<li><b>Security &amp; Governance:</b> Themen wie Identit&auml;ten, Berechtigungen, Secrets, Logging, Auditierung und Compliance denkst du, insbesondere in regulierten Umgebungen, von Anfang an mit.</li>
<li><b>Kommunikation &amp; Arbeitsweise: </b>Du vermittelst komplexe technische Zusammenh&auml;nge verst&auml;ndlich, arbeitest pragmatisch und bewegst dich auch in dynamischen Projektumfeldern sicher.</li>
<li><b>Pluspunkt:</b> Erfahrung mit vLLM, SGLang oder vergleichbaren Inference-Technologien, GPU-Clustern, souver&auml;nen Cloud- oder Private-Cloud-Umgebungen.</li>
<li><b>On the road:</b> Du bist reisebereit und flexibel, unsere Kunden bundesweit vor Ort zu beraten.
</li><li><b>Let's talk: </b>Du sprichst flie&szlig;end Deutsch und Englisch - super, dann findest du dich bei Exxeta bestens zurecht</li>
</ul></div></div><h3 class="jobSectionHeader"><b>Warum Exxeta</b></h3>
<div><div><p>Bei Exxeta entwickeln wir digitale L&ouml;sungen, die wirklich etwas ver&auml;ndern &ndash; in Unternehmen, M&auml;rkten und K&ouml;pfen. &Uuml;ber 1200 Kolleg:innen bringen daf&uuml;r Technologie, Ideen und unterschiedliche Perspektiven zusammen. Was uns antreibt: Neugier, Teamspirit und der Anspruch, echten Impact zu schaffen. Hightech with a heartbeat eben.</p>
<p>Wir sind ein Zuhause f&uuml;r Menschen, die etwas bewegen wollen. Diversit&auml;t und unterschiedliche Perspektiven bereichern unser Team. Haltung, Ideen und Lust aufs Machen - das z&auml;hlt bei uns!</p>
<p><b>Bewirb dich jetzt!</b></p>
</div></div></div></div></div><div><div>Deine Kontaktperson</div>
<div><div><div><div><div><h3 class="jobSectionHeader"><b>Sandra Walter</b></h3>
<div>Senior Talent Acquisition Partner</div></div><p></p><div><div><br>
</div></div></div></div></div></div></div><div><div><div><div><h2 class="jobSectionHeader"><b>Deine Benefits</b></h2>
</div><div><div><div><h3 class="jobSectionHeader"><b>An apple a day &hellip;</b></h3>
<p>Kaffee, Tee, Cola, Obst &ndash; geht aufs Haus</p>
</div></div><div><div><h3 class="jobSectionHeader"><b>Einfach mal weg?</b></h3>
<p>Bei uns normal: 30 Tage Urlaub und dein Sabbatical</p>
</div></div><div><div><h3 class="jobSectionHeader"><b>Flexibles Arbeiten</b></h3>
<p>Ob B&uuml;ro, Remote oder bei Kunden vor Ort? Deine Arbeitsweise gestaltet sich nach Kundenbedarf und Projektanforderung</p>
</div></div><div><div><h3 class="jobSectionHeader"><b>Gut ausgestattet</b></h3>
<p>Bei uns arbeitest du mit moderner Hardware. Extra Hardware leasen? Kein Problem</p>
</div></div><div><div><h3 class="jobSectionHeader"><b>Immer mobil</b></h3>
<p>Sportlich unterwegs: Wir bezuschussen dein (E-)Bike</p>
</div></div><div><div><h3 class="jobSectionHeader"><b>Lifelong Learning</b></h3>
<p>Workshops &amp; Mentoring: Gemeinsam entwickeln wir uns weiter</p>
</div></div><div><div><h3 class="jobSectionHeader"><b>Unvergessliche Events</b></h3>
<p>Unsere Erfolge feiern wir am liebsten als Team</p>
</div></div><div><div><h3 class="jobSectionHeader"><b>Versicherung inklusive</b></h3>
<p>Bis zu 1200 Euro Zuschuss f&uuml;r deine Brille, Z&auml;hne oder Massage</p></div></div></div></div></div></div></div>

AI Inference Engineer (all genders)

Perplexity

<div>
 We are looking for an AI Inference Engineer to join our growing team. We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL.
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 <strong>
  Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway.
  </li>
  <li>
   GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow.
  </li>
  <li>
   Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic.
  </li>
  <li>
   Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernels interleaving.
  </li>
  <li>
   Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Who We're Looking For
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus.
  </li>
  <li>
   You understand modern LLM architectures and are able to bring them up reliably in a production environment.
  </li>
  <li>
   You've built and operated production distributed systems under real load - ideally performance-critical ones.
  </li>
  <li>
   Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels.
  </li>
  <li>
   You own problems end-to-end. You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday.
  </li>
  <li>
   Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Nice-to-have
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   ML compilers and framework internals: PyTorch internals, torch.compile, custom operators.
  </li>
  <li>
   Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism.
  </li>
  <li>
   Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving.
  </li>
  <li>
   Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis.
  </li>
  <li>
   Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems.
  </li>
  <li>
   Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow).
  </li>
  <li>
   Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores).
  </li>
  <li>
   Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation).
   <br/>
   <br/>
  </li>
 </ul>
 Final offer amounts are determined by multiple factors including experience and expertise.
 <br/>
 <br/>
 <em>
  Equity: In addition to the base salary, equity may be part of the total compensation package.
  <br/>
  <br/>
 </em>
</div>


Member of Technical Staff (AI Inference Engineer)

Qualcomm

<div>
 <strong>
  Company
  <br/>
  <br/>
 </strong>
 Qualcomm Middle East Information Technology Company LLC
 <br/>
 <br/>
 <strong>
  Job Area
  <br/>
  <br/>
 </strong>
 Engineering Group, Engineering Group &gt; Software Engineering
 <br/>
 <br/>
 <strong>
  General Summary
  <br/>
  <br/>
 </strong>
 <strong>
  About Us
  <br/>
  <br/>
 </strong>
 Qualcomm is enabling a world where everyone and everything can be intelligently connected. You interact with products and technologies made possible by Qualcomm every day, including intelligent edge devices, next-generation computing platforms, and advanced AI solutions. Qualcomm&rsquo;s leadership in AI, high‑performance compute, and connectivity is driving innovation across cloud, edge, and data center environments - delivering scalable, power‑efficient platforms that power the next generation of intelligent infrastructure.
 <br/>
 <br/>
 <strong>
  About The Role
  <br/>
  <br/>
 </strong>
 Qualcomm is seeking a
 <strong>
  Machine Learning Operations &amp; Customer Support Engineer
 </strong>
 within the Customer Engineering team to support strategic customers deploying AI inference workloads on advanced Qualcomm AI inference accelerators. These accelerators utilize Qualcomm's expertise in hardware-accelerated AI to deliver high-performance, energy-efficient generative AI and computer vision inference solutions for modern data centers.
 <br/>
 <br/>
 This is a
 <strong>
  customer-facing, production-critical role
 </strong>
 focused on ensuring maximum system uptime, reliability, and performance, while resolving customer support cases within defined SLAs/KPIs. The role requires deep expertise across ML inference pipelines, systems troubleshooting, and data center operations, working closely with customers, internal engineering, and product teams.
 <br/>
 <br/>
 The ideal candidate will bring a strong foundation in ML model deployment, systems engineering, rack-scale management software, DevOps/MLOps automation, and cross functional collaboration.
 <br/>
 <br/>
 <strong>
  What You&rsquo;ll Do
  <br/>
  <br/>
 </strong>
 <strong>
  Customer Support &amp; SLA Ownership
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Act as the primary technical escalation point for customer issues related to AI inference workloads
  </li>
  <li>
   Own end-to-end case management, ensuring resolution within agreed SLAs and KPIs
  </li>
  <li>
   Drive incident response, triage, and root cause analysis (RCA).
  </li>
  <li>
   Provide timely and transparent communication to customers on issue status and resolution
  </li>
  <li>
   Maintain high levels of customer satisfaction and service reliability
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Uptime, Reliability &amp; Operations
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Ensure high availability and uptime of customer AI deployments (rack-scale systems)
  </li>
  <li>
   Monitor system health, performance metrics, and workload behavior
  </li>
  <li>
   Implement and manage failover, redundancy, and resiliency mechanisms
  </li>
  <li>
   Proactively identify risks and implement preventative actions
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   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  AI Inference Workload Support
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Support deployment, optimization, and troubleshooting of ML inference pipelines
  </li>
  <li>
   Debug issues across model, runtime, system, and hardware layers
  </li>
  <li>
   Analyze model performance (latency, throughput, accuracy tradeoffs) in production
  </li>
  <li>
   Support frameworks such as PyTorch, TensorFlow, ONNX, and model conversion flows
  </li>
  <li>
   Assist in model optimization techniques (quantization, batching, compilation, runtime tuning)
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   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  System &amp; Infrastructure Engineering
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  <br/>
 </strong>
 <ul>
  <li>
   Support bare-metal and virtualized environments for AI workloads
  </li>
  <li>
   Troubleshoot issues across Linux OS, drivers, firmware, and networking stack
  </li>
  <li>
   Support deployment and maintenance using Infrastructure as Code (IaC) and automation tools
  </li>
  <li>
   Work with DCIM tools and monitoring systems for infrastructure visibility
  </li>
  <li>
   Coordinate with hardware vendors for accelerator, server, and networking issues
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   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Monitoring, Observability &amp; Automation
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  <br/>
 </strong>
 <ul>
  <li>
   Implement and manage monitoring systems (logs, metrics, traces)
  </li>
  <li>
   Build dashboards for uptime, SLA adherence, performance, and utilization
  </li>
  <li>
   Automate repetitive operational tasks using scripts and workflows
  </li>
  <li>
   Establish and enforce runbooks and standard operating procedures (SOPs)
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   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Cross-Functional Collaboration
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  <br/>
 </strong>
 <ul>
  <li>
   Work closely with Customer Engineering, Product, Engineering, and Support teams
  </li>
  <li>
   Provide structured feedback to engineering for product improvements and defect resolution
  </li>
  <li>
   Support customer onboarding, deployment readiness, and operational handover
  </li>
  <li>
   Participate in customer reviews, escalations, and technical deep dives
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   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Required Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Bachelor&rsquo;s degree in Computer Science, Computer Engineering, Electrical Engineering, or related field
  </li>
  <li>
   10&ndash;15+ years of experience in ML operations, systems engineering, or customer support engineering
  </li>
  <li>
   Proven experience in customer-facing technical roles with SLA-driven support models
  </li>
  <li>
   Strong experience with AI/ML inference workloads in production environments
  </li>
  <li>
   Deep understanding of end-to-end ML inference pipelines
  </li>
  <li>
   Hands-on experience with Linux systems, system bring-up, drivers, and debugging tools
  </li>
  <li>
   Strong understanding of AI accelerator architecture and system bottlenecks
  </li>
  <li>
   Experience with model deployment, optimization, and performance tuning
  </li>
  <li>
   Experience with data center operations and rack-scale deployments
  </li>
  <li>
   Familiarity with bare-metal, virtualization, and containerization technologies (Docker, Kubernetes)
  </li>
  <li>
   Knowledge of networking concepts (TCP/IP, RDMA, storage systems)
  </li>
  <li>
   Experience with cloud and hybrid environments
  </li>
  <li>
   Experience with monitoring/observability tools (Prometheus, Grafana, ELK, etc.)
  </li>
  <li>
   Strong skills in incident management, RCA, and production operations
  </li>
  <li>
   Experience defining and tracking SLAs, KPIs, and operational metrics
  </li>
  <li>
   Proficiency in Python, Bash, or similar scripting languages
  </li>
  <li>
   Experience in automation, DevOps, and MLOps tooling
  </li>
  <li>
   Strong problem-solving and diagnostic skills
  </li>
  <li>
   Excellent communication and customer engagement skills
  </li>
  <li>
   Ability to operate in high-pressure, mission-critical environments
  </li>
  <li>
   High attention to detail with a focus on quality, reliability, and accountability
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience with Qualcomm Cloud AI or similar AI accelerator platforms
  </li>
  <li>
   Experience supporting large-scale AI deployments (LLMs, CV pipelines, generative AI)
  </li>
  <li>
   Familiarity with inference runtimes (TensorRT, ONNX Runtime, custom runtimes)
  </li>
  <li>
   Experience with CI/CD pipelines for ML deployment
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Why Join Qualcomm
  <br/>
  <br/>
 </strong>
 At Qualcomm, you&rsquo;ll work at the intersection of
 <strong>
  AI silicon, system architecture, and real
 </strong>
 ‑
 <strong>
  world deployment
 </strong>
 . You will engage directly with strategic customers, influence next‑generation AI data center platforms, and help define scalable, power‑efficient infrastructure for the AI era. This role provides a unique opportunity to shape both
 <strong>
  technology direction and customer outcomes
 </strong>
 , while working with world‑class engineering and product teams.
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 <br/>
 <strong>
  What's On Offer
  <br/>
  <br/>
 </strong>
 Apart from working with great people, we offer the below:
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 <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
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   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Minimum Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 OR
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 <br/>
 Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.
 <br/>
 <br/>
 OR
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 <br/>
 PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
 <br/>
 <br/>
 <ul>
  <li>
   2+ years of work experience with Programming Language such as C, C++, Java, Python, etc.
  </li>
  <li>
   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.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 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).
 <br/>
 <br/>
 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.
 <br/>
 <br/>
 <strong>
  To all Staffing and Recruiting Agencies
 </strong>
 : 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.
 <br/>
 <br/>
 If you would like more information about this role, please contact Qualcomm Careers.
</div>


ML Operations & Customer Support Engineer, Staff/Senior Staff level - Riyadh, KSA

d-Matrix

<div><p>At <b>d-Matrix</b>, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of <b>respect and collaboration.</b></p><p>
We value humility and believe in direct communication. Our <b>team is inclusive</b>, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution. Ready to come find your playground? <b>Together</b>, we can help shape the endless possibilities of AI.</p><p>
D-Matrix Frontier Group sits at the leading edge of what&rsquo;s possible with LLM inference on heterogeneous hardware. Our charter spans the full stack: from pathfinding emerging use cases and novel deployment patterns to deep optimization of inference kernels, to building proof-of-concept systems that showcase D-Matrix&rsquo;s unique computational fabric. We are an applied research and engineering team that moves fast, ships real systems, and works directly with product and hardware teams to shape the roadmap.</p><p>
We build the tools, runtimes, and frameworks that let frontier AI models run efficiently and cost-effectively across heterogeneous deployments &mdash; combining D-Matrix silicon with CPUs, GPUs, and custom accelerators. Our work powers everything from benchmarking and evaluation pipelines to production-grade inference serving.</p><p>
This Role</p><p>
We are hiring end-to-end inference engineers who are comfortable going from a novel research idea to a deployed, optimized system. You will work at every layer of the inference stack &mdash; from kernel-level optimization to distributed orchestration to high-level serving APIs.</p><p>
This role could be a great match for you if you:</p><ul><li>Have deep intuition for modern generative AI architectures and how to squeeze performance out of them at inference time.</li><li>Are familiar with the internals of open-source inference frameworks (vLLM, SGLang, TensorRT-LLM, etc.) and can extend or replace them when needed.</li><li>Enjoy pathfinding new use cases &mdash; exploring heterogeneous deployment topologies and building early-stage POCs that prove out new ideas.</li><li>Are results-oriented with a strong bias toward action; you own problems end-to-end from prototype to optimization to handoff.</li><li>Are energized by working at the intersection of novel hardware and frontier models, and want your work to directly influence how next-generation AI silicon is used.</li><li>Value clear communication and thrive in a small, high-ownership team environment.</li></ul><p>
Responsibilities</p><ul><li>Identify and prototype emerging LLM inference use cases suited to heterogeneous hardware deployments.</li><li>Build compelling proof-of-concept systems that demonstrate D-Matrix capabilities to customers, partners, and internal stakeholders.</li><li>Develop and tune custom kernels and operator-level optimizations to maximize throughput and minimize latency.</li><li>Drive quantization, sparsity, and batching strategies tailored to D-Matrix computational model.</li><li>Build and maintain inference runtimes, serving frameworks, and evaluation tooling.</li><li>Contribute to distributed inference systems: tensor/pipeline parallelism, disaggregated prefill/decode, KV-cache management.</li><li>Work closely with hardware architects to provide firmware and compiler teams with actionable inference workload insights.</li><li>Partner with product and business development to translate POCs into customer-facing demonstrations.</li><li>Contribute to technical publications, whitepapers, and open-source projects that advance D-Matrix visibility.</li></ul><p>
Required Qualifications</p><p></p><ul><li>Bachelor&rsquo;s degree in Computer Science, Electrical Engineering, or a related field, and 10+ years of relevant engineering experience; or equivalent demonstrated experience.</li><li>Master&rsquo;s or PhD in Computer Science, Electrical Engineering, or a related field preferred, with 6+ years of relevant industry experience.</li><li>Strong proficiency in Python and C/C++.</li><li>Hands-on experience optimizing LLM inference &mdash; attention kernels, KV cache, batching strategies, quantization (INT8/FP8/INT4).</li><li>Experience with at least one major inference framework (vLLM, SGLang, TensorRT-LLM, ONNX Runtime, or similar) at a contributor level.</li><li>Familiarity with GPU kernel programming (CUDA/Triton) and performance profiling tools.</li></ul><p>
Preferred Qualifications</p><ul><li>Experience with heterogeneous compute deployments &mdash; scheduling inference workloads across dissimilar hardware (accelerators, CPUs, GPUs).</li><li>Familiarity with custom silicon or ASIC-based inference (beyond GPU-only environments).</li><li>Experience with distributed inference: tensor parallelism, pipeline parallelism, disaggregated serving.</li><li>Contributions to open-source inference or ML systems projects.</li><li>Experience with production inference serving at scale (latency SLOs, continuous batching, multi-model serving).</li><li>Familiarity with speculative decoding, mixture-of-experts routing, or long-context serving techniques.</li><li>Working familiarity with the material in the JAX Scaling Book or equivalent systems-level understanding of modern LLM training and inference.</li></ul><p>
Why D-Matrix Frontier Group</p><ul><li>Work on genuinely novel hardware &mdash; D-Matrix in-memory compute architecture opens up inference optimization problems that don&rsquo;t exist anywhere else.</li><li>End-to-end ownership from idea to deployed system, with a short feedback loop between your work and real hardware.</li><li>Small, senior team with high autonomy and direct influence on product direction.</li><li>Competitive compensation, equity, and benefits in Santa Clara, CA.</li></ul><div></div><p><b><br>
Equal Opportunity Employment Policy</b></p><p><b>
d-Matrix</b> is proud to be an equal opportunity workplace and affirmative action employer. We&rsquo;re committed to fostering an inclusive environment where everyone feels welcomed and empowered to do their best work. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn together every day.</p><p><b>
d-Matrix</b> does not accept resumes or candidate submissions from external agencies. We appreciate the interest and effort of recruitment firms, but we kindly request that individual interested in opportunities with d-Matrix apply directly through our official channels. This approach allows us to streamline our hiring processes and maintain a consistent and fair evaluation of al applicants. Thank you for your understanding and cooperation.
</p><p>Compensation Range: $195K - $285K</p></div>

Principal LLM Inference Engineer

<div>
 <strong>
  Company
  <br/>
  <br/>
 </strong>
 Qualcomm Middle East Information Technology Company LLC
 <br/>
 <br/>
 <strong>
  Job Area
  <br/>
  <br/>
 </strong>
 Engineering Group, Engineering Group &gt; Systems Engineering
 <br/>
 <br/>
 <strong>
  General Summary
  <br/>
  <br/>
 </strong>
 <strong>
  About Us
  <br/>
  <br/>
 </strong>
 Qualcomm is growing its presence in Riyadh and is hiring Data Centre Engineers to support our expanding infrastructure across the region
 <strong>
  .
 </strong>
 As Saudi Arabia accelerates its digital transformation under Vision 2030, Qualcomm is investing in world‑class computing and data centre capabilities to power AI, cloud, and advanced connectivity at scale. This is a unique opportunity to work in a fast‑growing technology hub, supporting critical environments and helping shape the future of data centre operations in the Kingdom and beyond.
 <br/>
 <br/>
 <strong>
  About The Role
  <br/>
  <br/>
 </strong>
 As a Qualcomm Datacenter AI Solutions Engineer, you will research, develop, optimize, and validate software, hardware, architecture, algorithms, and machine learning solutions that enable the deployment of cutting-edge AI datacenter technology.
 <br/>
 <br/>
 Qualcomm Solution Engineers collaborate across functional teams to meet and exceed system-level requirements and standards. This is a great opportunity to innovate and develop leading-edge products and solutions around best-in-class Qualcomm AI inference accelerators for data center, and hybrid AI applications.
 <br/>
 <br/>
 <strong>
  Principal Duties And Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Design, develop, and optimize AI/ML solutions leveraging Qualcomm AI hardware, software, and ecosystem to deliver high-performance, power‑efficient, and scalable inference
  </li>
  <li>
   Develop and deploy GenAI and LLM-based applications, including model integration, fine-tuning, and optimization
  </li>
  <li>
   Apply systems-level knowledge to analyze, design, implement, and validate AI software, models, and system architecture
  </li>
  <li>
   Perform AI model benchmarking, functional analysis, and performance characterization to drive requirements and improvements
  </li>
  <li>
   Support customer engagements by optimizing AI models and workloads for deployment and inference efficiency
  </li>
  <li>
   Contribute to system-level design efforts, including requirements definition, interfaces, performance targets, and implementation
  </li>
  <li>
   Collaborate closely with cross-functional teams to implement, test, and validate AI system features
  </li>
  <li>
   Debug and triage system-level issues, perform root-cause analysis, and communicate findings clearly
  </li>
  <li>
   Stay current with advancements in AI/ML models, tools, and hardware, and apply innovative ideas to products and solutions
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Master&rsquo;s or PhD in Engineering, Computer Science, Information Systems, or a related field
  </li>
  <li>
   Proficiency in Python and experience with ML frameworks, APIs, REST services, and microservice-based architecture
  </li>
  <li>
   Hands-on experience building, deploying, and tuning AI/ML systems
  </li>
  <li>
   Strong understanding of GenAI architectures (e.g., transformers, diffusion models, LLMs, embeddings)
  </li>
  <li>
   Experience with large-scale AI system design, including distributed systems, fault tolerance, and event-driven architectures
  </li>
  <li>
   Familiarity with MLOps practices and ML lifecycle management
  </li>
  <li>
   Experience with containerization and automation tools (Docker, Kubernetes, Git/GitOps)
  </li>
  <li>
   Experience with model fine-tuning and/or reinforcement learning is a plus
  </li>
  <li>
   Familiarity with observability, benchmarking, and performance analysis for ML pipelines is a plus
  </li>
  <li>
   Experience working in large, matrixed organizations and collaborating across teams
  </li>
  <li>
   Strong problem-solving skills and customer-focused mindset
  </li>
  <li>
   Experience with open-source development practices
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  What's On Offer
  <br/>
  <br/>
 </strong>
 Apart from working with great people, we offer the below:
 <br/>
 <br/>
 <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
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Minimum Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 6+ years of Systems Engineering or related work experience.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 OR
 <br/>
 <br/>
 Master's degree in Engineering, Information Systems, Computer Science, or related field and 5+ years of Systems Engineering or related work experience.
 <br/>
 <br/>
 OR
 <br/>
 <br/>
 PhD in Engineering, Information Systems, Computer Science, or related field and 4+ years of Systems Engineering or related work experience.
 <br/>
 <br/>
 <ul>
  <li>
   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.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 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).
 <br/>
 <br/>
 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.
 <br/>
 <br/>
 <strong>
  To all Staffing and Recruiting Agencies
 </strong>
 : 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.
 <br/>
 <br/>
 If you would like more information about this role, please contact Qualcomm Careers.
</div>


Datacenter AI Solutions Engineer (Customer Engagement) – Riyadh, KSA

Pika

<div><h3 class="jobSectionHeader"><b>About the Role</b></h3><p>
We are seeking a Senior Inference Engineer to accelerate the performance of Pika's AI-driven products. In this highly technical role, you will operate at the intersection of cutting-edge inference acceleration, GPU parallelism, advanced model deployment, and video generation technologies. Your expertise will drive significant improvements to model speed and efficiency, ensuring our creative AI systems deliver industry-leading user experiences at scale.</p><div></div><p><br>
You will design and optimize inference pipelines, implement state-of-the-art acceleration techniques, and work closely with researchers and engineers across the team to push the boundaries of what&rsquo;s possible in real-time AI deployment. Your efforts will play a foundational role in powering the next generation of Pika&rsquo;s video and language models.</p><div><br>
</div><h3 class="jobSectionHeader"><b>
What You&rsquo;ll Do</b></h3><ul><li><p><b>
Accelerate Inference</b>: Lead and implement advanced inference acceleration techniques, including attention optimization and quantization for efficient model serving.</p></li><li><p><b>
Maximize GPU Parallelism</b>: Engineer and optimize GPU strategies across tensor, sequence, and pipeline parallelism (TP, SP, PP) for maximal efficiency and scalability.</p></li><li><p><b>
Programming for Performance</b>: Develop and optimize high-performance computing kernels and distributed workloads using CUDA and NCCL.</p></li><li><p><b>
Advance AI Deployment</b>: Collaborate with research and engineering teams to bring state-of-the-art videogen and large language models into production.</p></li><li><p><b>
Improve Training Efficiency</b>: (Bonus) Contribute to improvements in model training speed, stability, and resource utilization as part of our deployment lifecycle.</p></li><li><p><b>
Technical Excellence</b>: Drive rigorous code reviews, participate in technical discussions, and mentor fellow engineers on best practices in inference and GPU programming.</p></li></ul><div><br>
</div><h3 class="jobSectionHeader"><b>
What We&rsquo;re Looking For</b></h3><ul><li><p><b>
Experience</b>: 5+ years engineering experience, with a strong track record in inference acceleration and model deployment at scale.</p></li><li><p><b>
Inference Mastery</b>: Proven expertise in inference optimization, including quantization, attention acceleration, and deep learning compiler stacks.</p></li><li><p><b>
GPU &amp; Parallelism</b>: Deep knowledge of GPU programming (CUDA, NCCL) and experience with SP, TP, PP, and other forms of parallelism for distributed inference.</p></li><li><p><b>
AI Domain Knowledge</b>: Familiarity with video generation (videogen) models and large language models (LLMs).</p></li><li><p><b>
Collaboration</b>: Strong cross-discipline communication skills; able to drive shared goals across research and engineering functions.</p></li><li><p><b>
Ownership Mindset</b>: Self-driven, solutions-oriented, and capable of managing ambiguity in a fast-paced startup environment.</p></li><li><p><b>
Bonus</b>: Experience in enhancing training efficiency, stability, or resource optimization for large models.</p></li></ul><div><br>
</div><h3 class="jobSectionHeader"><b>
Nice to Have</b></h3><ul><li><p>
Experience with high-throughput video or real-time streaming model deployment</p></li><li><p>
Familiarity with distributed training and optimization toolkits</p></li><li><p>
Contributions to open source projects in AI infrastructure or deep learning compilers</p></li><li><p>
Startup or rapid prototyping experience</p></li></ul><div><br>
</div><h3 class="jobSectionHeader"><b>
What We Offer</b></h3><ul><li><p>
Competitive salary in the AI industry</p></li><li><p>
Equity in a fast-growing startup shaping the future of AI</p></li><li><p>
Comprehensive health benefits, monthly stipends, company retreats</p></li><li><p>
A supportive and collaborative office culture&mdash;we&rsquo;re all building and launching together</p></li></ul><div><br>
</div><h3 class="jobSectionHeader"><b>
About Pika</b></h3><p>
At Pika, we're crafting a future where video creation is seamless, intuitive, and universally accessible. Our mission is to empower creativity by breaking down technical barriers using the transformative power of AI. We&rsquo;re a tight-knit, energetic team based in Palo Alto, CA, valuing efficiency, curiosity, and the ambition to make a meaningful impact on the world.</p><div></div><p><br>
We work from our Palo Alto office 3&ndash;5 days a week and welcome applicants who are eager to contribute onsite.</p><p>
Compensation Range: $185K - $250K</p></div>

Senior Software Engineer, Inference

AppliedAI

<div>
 <p>
  <strong>
   AI Engineer
  </strong>
 </p>
 <p>
  On-site | Abu Dhabi
 </p>
 <p>
  <br/>
 </p>
 <p>
  AppliedAI is a pioneering AI technology company headquartered in Abu Dhabi, committed to innovation and excellence in artificial intelligence solutions in regulated industries such as healthcare, insurance, government, and financial services.
  <strong>
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  Opus is the world's first
  <strong>
   Knowledge Work AI platform
  </strong>
  . Built by AppliedAI to pioneer
  <strong>
   Supervised Automation
  </strong>
  , a human-in-the-loop model where AI handles repetitive, structured tasks while human experts provide crucial oversight at defined intervals.
  <strong>
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  The platform uses its proprietary
  <strong>
   Large Work Model
  </strong>
  to generate and orchestrate outcome-based workflows, enabling a dramatic reduction in the cost of knowledge work and allowing human talent to focus on high-value, creative, and judgement-intensive activities.
  <strong>
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Role Overview:
  </strong>
  <strong>
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  As an Opus AI Engineer, you will contribute to the development, optimization, and reliability of Opus&rsquo;s AI infrastructure. You&rsquo;ll work in a cross-functional UAE-based team to build, maintain, and improve the systems that power Opus&rsquo;s AI features, from inference pipelines and workflow orchestration to prompt alignment and agentic integrations.
 </p>
 <p>
  <br/>
 </p>
 <p>
  Your focus will be on implementing clean, efficient, and observable systems that ensure Opus&rsquo;s AI features deliver consistent, high-quality performance for users.
 </p>
 <p>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Key Responsibilities:
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   Develop and maintain AI inference and orchestration pipelines used in production.
  </li>
  <li>
   Optimize model-serving performance, latency, and reliability across live systems.
  </li>
  <li>
   Implement observability, logging, and monitoring for AI components.
  </li>
  <li>
   Contribute to prompt and alignment improvements to enhance response quality and consistency.
  </li>
  <li>
   Refactor existing code for modularity, clarity, and maintainability.
  </li>
  <li>
   Collaborate with senior engineers and product teams to deliver stable, user-facing AI features.
  </li>
  <li>
   Participate in design reviews, testing, and rollout of new systems and improvements.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Qualifications:
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   2 - 4 years of experience in ML or backend engineering.
  </li>
  <li>
   1+ years working on LLM prompt design, model behavior shaping, or symbolic product-logic integration into language systems.
  </li>
  <li>
   Strong Python programming skills and understanding of production ML systems.
  </li>
  <li>
   Familiar with orchestration tools (Airflow, Celery, Kubernetes) and cloud environments (AWS, GCP, Azure).
  </li>
  <li>
   Experience deploying and monitoring inference pipelines (e.g. LLMs, CV models, or similar APIs).
  </li>
  <li>
   Knowledge of observability tools and performance debugging.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Why join AppliedAI:
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   Opportunity to work with a highly innovative AI technology company.
  </li>
  <li>
   Collaborative and innovative work environment.
  </li>
  <li>
   Growing, entrepreneurial and forward-thinking culture.
  </li>
  <li>
   Career growth and professional development opportunities.
  </li>
  <li>
   Exposure to a thriving ecosystem working from our Abu Dhabi HQ.
  </li>
 </ul>
</div>


AI Engineer

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