Jobgether تعلن عن وظيفة مهندس تعلم آلة - تحسين الاستدلال في السعودية
Machine Learning Engineer - Inference Optimization
🏢 Jobgether
تفاصيل الوظيفة
Jobgether تعلن بالنيابة عن شريكها عن وظيفة مهندس تعلم آلة - تحسين الاستدلال (Machine Learning Engineer - Inference Optimization) في السعودية. تهدف هذه الوظيفة إلى تحسين أداء أنظمة التعلم الآلي المتقدمة المستخدمة في بيئات الإنتاج، والعمل على تحويل النماذج الحديثة إلى حلول سريعة وموثوقة وفعالة من حيث التكلفة.
المهام والمسؤوليات
- تحسين أنظمة استدلال التعلم الآلي لتحسين زمن الاستجابة (Latency) والإنتاجية (Throughput) وقابلية التوسع (Scalability) والتكلفة التشغيلية.
- تحليل وتحديد الاختناقات في خطوط أنابيب الاستدلال على وحدة معالجة الرسوميات (GPU) ووحدة المعالجة المركزية (CPU)، بما في ذلك استخدام الذاكرة والنوى (Kernels) واستراتيجيات التجميع (Batching) وتدفق البيانات.
- تنفيذ تقنيات التحسين المتقدمة مثل التكميم (Quantization) وتحسين ذاكرة التخزين المؤقت للمفاتيح والقيم (KV-cache optimization) وفك الترميز التخميني (Speculative decoding) والتجميع والبث (Streaming) وتبسيط النماذج.
- التعاون مع مهندسي الأبحاث لإنتاج بنى النماذج الجديدة وترجمة النتائج التجريبية إلى أنظمة موثوقة.
- بناء وتحسين وصيانة البنية التحتية لخدمة الاستدلال باستخدام أطر العمل الحديثة أو بيئات التشغيل المخصصة أو حلول الخدمة المتخصصة.
- قياس أداء النماذج عبر بيئات الأجهزة المختلفة، بما في ذلك وحدات معالجة الرسوميات (GPU) ووحدات المعالجة المركزية (CPU) والأنظمة السحابية.
- تحسين موثوقية النظام والمراقبة (Monitoring) والمرئية (Observability) وكفاءة التكلفة تحت أحمال الإنتاج الفعلية.
- المساهمة في الممارسات الهندسية التي تحسن جودة البنية التحتية للتعلم الآلي وقابلية التوسع والصيانة.
الشروط والمتطلبات
- خبرة مهنية قوية في تحسين استدلال التعلم الآلي (ML inference optimization) أو أنظمة التعلم الآلي عالية الأداء أو المجالات ذات الصلة.
- فهم عميق لأساسيات التعلم الآلي، بما في ذلك بنى الشبكات العصبية وآليات الانتباه (Attention mechanisms) وتحسين الذاكرة ورسوم الحساب (Compute graphs).
- خبرة عملية مع PyTorch أو أطر التعلم العميق المماثلة ونشر النماذج في بيئات الإنتاج.
- خبرة في تحسين أداء وحدة معالجة الرسوميات (GPU)، بما في ذلك تقنيات مثل CUDA أو ROCm أو Triton أو ضبط النوى (Kernel-level tuning).
- خبرة مثبتة في توسيع نطاق أنظمة الاستدلال لمستخدمين حقيقيين تتجاوز النماذج الأولية أو المعايير المرجعية.
- مهارات برمجة قوية والقدرة على العمل عبر مجالات التعلم الآلي وهندسة النظم.
- القدرة على العمل بفعالية في بيئات سريعة الخطى مع روح الملكية والاستقلالية وتغير الأولويات.
المهارات المطلوبة
- خبرة مع أطر الاستدلال مثل TensorRT أو ONNX Runtime أو vLLM أو Triton (ميزة إضافية).
- الإلمام بنماذج اللغة الكبيرة (Large Language Models) والاستدلال طويل السياق (Long-context inference) والأنظمة الموزعة والخدمات منخفضة زمن الاستجابة (Low-latency services) أو تحسين الأجهزة (يعتبر ميزة).
- المساهمات في أنظمة التعلم الآلي مفتوحة المصدر أو أدوات الاستدلال (ميزة إضافية).
المزايا
- حزمة تعويضات تنافسية مع مشاركة أسهم ذات معنى.
- فرصة للعمل على أنظمة ذكاء اصطناعي حساسة للأداء ذات تأثير مباشر على المنتج.
- درجة عالية من الملكية على البنية التحتية التي تشكل قابلية التوسع والكفاءة.
- تعاون وثيق مع فرق الأبحاث والبنية التحتية والمنتجات.
- فرصة للعمل على تقنيات التعلم الآلي المتقدمة وتطبيقات الذكاء الاصطناعي الواقعية.
- ثقافة هندسية تركز على التميز التقني والتجريب والجودة.
- بيئة عمل مرنة عن بُعد.
- فرصة للمساهمة في نمو منظمة مبتكرة تركز على الذكاء الاصطناعي.
عرض النص الأصلي للإعلان
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer - Inference Optimization based in Saudi Arabia.
This role offers the opportunity to optimize the performance of advanced machine learning systems used in real-world production environments.
You will work at the intersection of research and engineering, transforming cutting-edge models into fast, reliable, and cost-efficient solutions.
Your work will directly impact model scalability, user experience, and the efficiency of AI-powered products.
You will dive deep into performance optimization, from model architecture and GPU execution to large-scale inference infrastructure.
Working with talented research, infrastructure, and product teams, you will help push the boundaries of what AI systems can achieve.
This position is ideal for an engineer who enjoys solving complex technical challenges and building high-performance ML systems from the ground up.
Accountabilities
As a Machine Learning Engineer specializing in inference optimization, you will own the performance and scalability of machine learning models in production. You will combine deep ML expertise, systems engineering, and performance analysis to deliver faster, more efficient AI experiences.
The ideal candidate is a technically strong machine learning engineer with experience optimizing production inference systems and a passion for high-performance AI engineering. You should enjoy working on complex technical problems, experimenting with new approaches, and taking ownership of critical systems.
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
This role offers the opportunity to optimize the performance of advanced machine learning systems used in real-world production environments.
You will work at the intersection of research and engineering, transforming cutting-edge models into fast, reliable, and cost-efficient solutions.
Your work will directly impact model scalability, user experience, and the efficiency of AI-powered products.
You will dive deep into performance optimization, from model architecture and GPU execution to large-scale inference infrastructure.
Working with talented research, infrastructure, and product teams, you will help push the boundaries of what AI systems can achieve.
This position is ideal for an engineer who enjoys solving complex technical challenges and building high-performance ML systems from the ground up.
Accountabilities
As a Machine Learning Engineer specializing in inference optimization, you will own the performance and scalability of machine learning models in production. You will combine deep ML expertise, systems engineering, and performance analysis to deliver faster, more efficient AI experiences.
- Optimize machine learning inference systems to improve latency, throughput, scalability, and operational cost.
- Profile and identify bottlenecks across GPU and CPU inference pipelines, including memory usage, kernels, batching strategies, and data flow.
- Implement advanced optimization techniques such as quantization, KV-cache optimization, speculative decoding, batching, streaming, and model simplification.
- Collaborate with research engineers to productionize new model architectures and translate experimental results into reliable systems.
- Build, improve, and maintain inference-serving infrastructure using modern frameworks, custom runtimes, or specialized serving solutions.
- Benchmark model performance across different hardware environments, including GPUs, CPUs, and cloud-based systems.
- Improve system reliability, monitoring, observability, and cost efficiency under real production workloads.
- Contribute to engineering practices that improve the quality, scalability, and maintainability of ML infrastructure.
The ideal candidate is a technically strong machine learning engineer with experience optimizing production inference systems and a passion for high-performance AI engineering. You should enjoy working on complex technical problems, experimenting with new approaches, and taking ownership of critical systems.
- Strong professional experience in ML inference optimization, high-performance machine learning systems, or related areas.
- Deep understanding of machine learning fundamentals, including neural network architectures, attention mechanisms, memory optimization, and compute graphs.
- Hands-on experience with PyTorch or similar deep learning frameworks and deploying models into production environments.
- Experience with GPU performance optimization, including technologies such as CUDA, ROCm, Triton, or kernel-level tuning.
- Proven experience scaling inference systems for real users beyond research prototypes or benchmarks.
- Strong programming skills and the ability to work across machine learning and systems engineering domains.
- Ability to operate effectively in fast-paced environments with ownership, autonomy, and evolving priorities.
- Experience with inference frameworks such as TensorRT, ONNX Runtime, vLLM, or Triton is a plus.
- Familiarity with large language models, long-context inference, distributed systems, low-latency services, or hardware optimization is considered an advantage.
- Contributions to open-source ML systems or inference tooling are a plus.
- Competitive compensation package with meaningful equity participation.
- Opportunity to work on performance-critical AI systems with direct product impact.
- High level of ownership over infrastructure that shapes scalability and efficiency.
- Close collaboration with research, infrastructure, and product teams.
- Opportunity to work on advanced machine learning technologies and real-world AI applications.
- Engineering-focused culture that values technical excellence, experimentation, and quality.
- Flexible remote work environment.
- Opportunity to contribute to the growth of an innovative AI-focused organization.
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
المصدر: LinkedIn - أُضيفت للموقع في 24 يوليو 2026
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