Member of Technical Staff, Multimodal Infrastructure (AI)

TL;DR

Member of Technical Staff, Multimodal Infrastructure (AI): Building and optimizing large-scale infrastructure to support the full cycle of multimodal generative model development with an accent on data processing, training, inference, and serving frameworks. Focus on driving architectural changes, implementing best practices, and ensuring system reliability for cutting-edge AI products.

Location: Mountain View, United States. Employees within 50 miles of a designated Microsoft office in the U.S. are expected to work from the office at least four days a week.

Salary: USD $139,900 – $274,800 per year (IC5, U.S. average); USD $188,000 – $304,200 per year (IC5, San Francisco Bay Area/NYC); USD $163,000 – $296,400 per year (IC6, U.S. average); USD $220,800 – $331,200 per year (IC6, San Francisco Bay Area/NYC).

Company

hirify.global is an organization dedicated to advancing Copilot and other consumer AI products and research, responsible for Copilot, Bing, Edge, and generative AI research.

What you will do

• Design, develop, and maintain large-scale multimodal data processing pipelines.
• Design, develop, and maintain large-scale multimodal model pretraining and post-training frameworks.
• Design, develop, and maintain large-scale multimodal model inference and serving frameworks.
• Collaborate closely with research scientists and product engineers to solve infrastructure-related problems.
• Drive architectural changes and influence the roadmap of relevant software and hardware components.

Requirements

• Bachelor’s Degree in Computer Science or a related technical discipline.
• 6+ years of technical engineering experience with coding in languages including C, C++, C#, Java, JavaScript, or Python.
• Experience in multi-modal data processing, including distributed data processing infrastructure (Ray & Spark) and CPU/GPU batch processing optimizations.
• Strong proficiency in deep learning frameworks such as PyTorch, Megatron, and Deepspeed.
• Knowledge of auto-regressive and diffusion transformer models.
• Experience with distributed training techniques (data parallelism, model parallelism, pipeline parallelism).
• English: B2 required.

Nice to have

• Master’s Degree in Computer Science or a related technical field with 8+ years experience, or 10+ years technical engineering experience.
• Experience with state-of-the-art model inference and serving frameworks.
• Experience with image/video/audio data processing and common data formats for efficient I/O.
• Proven experience in image/video generation and editing, efficient architectures (e.g., MoE), efficient model design, or reinforcement learning training methods (e.g., RLHF, DPO).
• Strong proficiency in serving frameworks such as vLLM, TensorRT-LLM, SGLang, xDiT, Cache-DiT.
• Knowledge of distillation techniques (Progressive Distillation, DMD, Self forcing) and quantization/compression techniques (AWQ, GPTQ, FP8).
• Experience in distributed inference scaling across multi-node clusters using Ray Serve and Triton.
• Experience in leading technical projects and supporting architectural decisions with data.

Culture & Benefits

• Be part of a team shaping the future of personal computing through advanced AI.
• Work in a fast-paced environment building world-class consumer experiences and products.
• Opportunity to embody a culture of inclusion, respect, integrity, and accountability.
• Contribute to Microsoft’s mission to empower every person and organization.