As a key member of our Search team, you will be responsible for pushing search quality forward through models, data, tools, or any other leverage available. This includes architecting and building core components of the search platform and model stack, designing, training, and optimizing large-scale deep learning models using frameworks like PyTorch, and deploying models in a scalable and performant way.

Responsibilities:

• Relentlessly push search quality forward , through models, data, tools, or any other leverage available
• Architect and build core components of the search platform and model stack
• Design, train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models
• Conduct advanced research in representation learning, including contrastive learning, multilingual, and multimodal modeling for search and retrieval
• Deploy models , from boosting algorithms to LLMs , in a scalable and performant way
• Build and optimize RAG pipelines for grounding and answer generation
• Collaborate with Data, AI, Infrastructure, and Product teams to ensure fast and high-quality delivery

Qualifications:

• Deep understanding of search and retrieval systems, including quality evaluation principles and metrics
• Proven track record with large-scale search or recommender systems
• Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models
• Expertise in representation learning, including contrastive learning and embedding space alignment for multilingual and multimodal applications
• Strong publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, SIGIR)
• Self-driven, with a strong sense of ownership and execution
• Minimum of 3 years (preferably 5+) working on search, recommender systems, or closely related research areas

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Member of Technical Staff - Image / Video Generation

Job Description

We're the team behind Latent Diffusion, Stable Diffusion, and FLUX,foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video,tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we’re just getting started.

Why This Role

You'll train large-scale diffusion models for image and video generation, exploring new approaches while maintaining the rigor that helps us distinguish meaningful progress from incremental tweaks. This isn't about following established recipes,it's about running the experiments that clarify which architectural choices matter and which are less impactful.

What You’ll Work On

• Trains large-scale diffusion transformer models for image and video data, working at the scale where intuitions break and empirical evidence matters
• Rigorously ablates design choices,running experiments that isolate variables, control for confounds, and produce insights you can actually trust,then communicating those results to shape our research direction
• Reasons about the speed-quality tradeoffs of neural network architectures in production settings where both constraints matter simultaneously
• Fine-tunes diffusion models for specialized applications like image and video upscalers, inpainting/outpainting models, and other tasks where general-purpose models aren't enough

What We’re Looking For

• You've trained large-scale diffusion models and developed strong intuitions about what matters. You know that at research scale, every design choice has tradeoffs, and the only way to know which ones are worth making is through careful ablation. You're comfortable debugging distributed training issues and presenting research findings to the team.

Required Skills

• Hands-on experience training large-scale diffusion models for image and video data, with practical knowledge of common failure modes and what matters most in training
• Experience fine-tuning diffusion models for specialized applications,upscalers, inpainting, outpainting, or other tasks where understanding the domain matters as much as understanding the architecture
• Deep understanding of how to effectively evaluate image and video generative models,knowing which metrics correlate with quality and which are just convenient proxies
• Strong proficiency in PyTorch, transformer architectures, and the full ecosystem of modern deep learning
• Solid understanding of distributed training techniques,FSDP, low precision training, model parallelism,because our models don't fit on one GPU and training decisions impact research outcomes

Preferred Skills

• Experience writing forward and backward Triton kernels and ensuring their correctness while considering floating point errors
• Proficiency with profiling, debugging, and optimizing single and multi-GPU operations using tools like Nsight or stack trace viewers
• Know the performance characteristics of different architectural choices at scale
• Have published research that contributed to how people think about generative models

How We Work Together

We’re a distributed team with real offices that people actually use. Depending on your role, you’ll either join us in Freiburg or SF at least 2 days a week (or one full week every other week), or work remotely with a monthly in-person week to stay connected. We’ll cover reasonable travel costs to make this possible. We think in-person time matters, and we’ve structured things to make it accessible to all. We’ll discuss what this will look like for the role during our interview process.

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Microsoft AI are looking for a talented Member of Technical Staff, Multimodal Infrastructure to help build the next wave of capabilities of our personalized AI assistant, Copilot. We're looking for someone who will bring an abundance of positive energy, empathy, and kindness to the team every day, in addition to being highly effective.

About the Role

We're looking for someone who will design, develop and maintain large-scale multimodal data processing pipelines, model pretraining and post-training frameworks, and model inference and serving frameworks. You will work closely with research scientists and product engineers on multimodal data processing, model training, inference and serving tasks. As a contributing member of the core group of engineers, you would also bring to the table best practices driving architectural changes and influence roadmap of relevant software and hardware components.

Accountabilities

• 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.

The Candidate we're looking for

Experience:

• Bachelor's Degree in Computer Science, or related technical discipline AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.

Technical skills:

• Strong proficiency in distributed data processing infra (resource utilization management, fault tolerance, ray & spark) and CPU/GPU batch processing optimizations.
• Experience with state-of-art model inference and serving frameworks.
• Experience with image/video/audio data processing.
• Experience with common data formats for efficient I/O.

Personal attributes:

• Enjoy working in a fast-paced, design-driven, product development cycle.
• Embody our Culture and Values.

Benefits

• Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location.
• Comprehensive health and wellbeing benefits.
• Professional development opportunities.
• Financial benefits (bonus, equity, pension, etc.).

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Microsoft AI are looking for a talented Member of Technical Staff, Multimodal Infrastructure to help build the next wave of capabilities of our personalized AI assistant, Copilot. We’re looking for someone who will bring an abundance of positive energy, empathy, and kindness to the team every day, in addition to being highly effective.

About the Role

As a Member of Technical Staff, Multimodal Infrastructure, you will be responsible for designing, developing, and maintaining large-scale multimodal data processing pipelines, model pretraining and post-training frameworks, and model inference and serving frameworks. You will work closely with research scientists and product engineers to solve infra-related problems and find a path to get things done despite roadblocks to get your work into the hands of users quickly and iteratively.

Accountabilities

• 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.

The Candidate we're looking for

Experience:

• Bachelor’s Degree in Computer Science, or related technical discipline AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.

Technical skills:

• Strong proficiency in distributed data processing infra (resource utilization management, fault tolerance, ray & spark) and CPU/GPU batch processing optimizations.
• Experience with state-of-art model inference and serving frameworks.
• Experience with image/video/audio data processing.
• Experience with common data formats for efficient I/O.

Personal attributes:

• Enjoy working in a fast-paced, design-driven, product development cycle.
• Embody our Culture and Values.

Benefits

• Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location.
• Comprehensive health and wellbeing benefits.
• Professional development opportunities.
• Financial benefits (bonus, equity, pension, etc.).

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