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Exhibit solid analytical skills, attention to detail, and a commitment to data-driven decision-making. Have experience and/or in-depth understandings about large-scale distributed systems. Demonstrate an ability to work collaboratively in a fast-paced, innovative environment. Bachelor's Degree in Computer Science, or related technical discipline AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Demonstrated experience in large-scale AI. Passionate about conversational AI and its deployment. Demonstrated written and verbal communication skills with the ability to work closely with cross-functional teams, including product managers, designers, and other engineers. Passion for learning new technologies and staying up to date with industry trends, best practices, and emerging technologies in AI. Proven ability to collaborate and contribute to a positive, inclusive work environment, fostering knowledge sharing and growth within the team. Develop algorithms, design model architectures, conduct experiments, champion measurement and evaluation, innovate datasets and data pipelines. Improve training and deployment efficiency, paying careful attention to detail, persevering, and learning from everyone's attempts whether successful or not. Follow a rigorous data-driven approach grounded in meticulous ablation studies and scientific analysis. Innovate and iterate over ideas, prototypes, and product. Collaborate closely with teams on infrastructure, data engineering, pre-training, post-training, and product feedback. Advance the AI frontier responsibly. 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Your expertise will help us create robust and scalable platforms that drive innovation and efficiency. Design, develop, and maintain platform-level software solutions. Collaborate with cross-functional teams to integrate AI capabilities into various products. Ensure the reliability, scalability, and performance of platform components. Stay updated with the latest advancements in AI and engineering. Work alongside the technical staff and AI researchers to improve model development flows. Embody our Culture and Values. Bachelor's degree in computer science, or related/equivalent technical discipline AND 4+ years technical engineering experience building and scaling web services, APIs, and products. OR equivalent experience. Deep experience with Python AND at least one of the following: Golang, Java/Scala, Typescript (React, Next.js) 4+ years' experience in building and releasing production software at the platform level. Strong knowledge of APIs, data flows, systems, and services. Bachelor's degree in computer science, or related technical discipline AND 6+ years technical engineering experience building web services with coding in languages including, but not limited to: Python, Golang, Java/Scala, Rust 6+ years' experience in building and releasing production software at the platform level. Deep experience with all of the following languages: Golang, Java/Scala, Typescript (React/Next.js) Experience in model pretraining, post-training, evaluation, and inference Experience using Machine Learning frameworks, including experience using, deploying, and scaling language learning models, either personally or professionally. Ability to clearly communicate complex technical concepts to both technical and non-technical stakeholders. Demonstrated interpersonal skills and ability to work closely with cross-functional teams, including product managers, designers, and other engineers. Proven ability to collaborate and contribute to a positive, inclusive work environment, fostering knowledge sharing and growth within the team. Experience going from zero-to-one as well as working with developed systems. 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We\u2019re looking for  someone  who possesses technical prowess, a methodical approach to problem-solving and proficiency in client technologies. The right candidate enjoys building world-class consumer experiences and products in a fast-paced environment. The Windows Copilot team is responsible for creating experiences that demonstrate the power Copilot can bring to Windows, large screens and productivity scenarios. In this role you will play a critical role in designing, developing, and implementing cutting-edge solutions while collaborating with Platform, Product Management, Design and Research teams.\u00a0\u00a0\u00a0 Our newly formed organization, MAI, is dedicated to advancing Copilot and other consumer AI products and research. The team\u00a0is responsible for\u00a0Copilot, Bing, Edge, and generative AI research.\u00a0Come be\u00a0a part of the team shaping the future personal computing.\u00a0 Microsoft\u2019s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.\u00a0 By applying to this U.S. Redmond, WA position, you are required to be local to the Seattle area in office 3 days a week. 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. This expectation is subject to local law and may vary by jurisdiction. Responsibilities Design and develop client applications and components to support Copilot experiences. Work collaboratively with platform, infrastructure, application engineers and researchers to build next generation AI products and services.\u00a0 Ship high-quality, well-tested, secure, and maintainable code.\u00a0\u00a0 Overcome obstacles to deliver work quickly and iteratively to users Enjoy working in a fast-paced, design-driven, product development cycle.\u00a0 Embody our\u202f Culture and\u202f Values .\u00a0 Qualifications Required Qualifications: Bachelor\u2019s degree in computer science, or related technical discipline AND 4+ years technical engineering experience building applications including, but not limited to, Python, C#, C++, .NET, XAML\u00a0 OR equivalent experience.\u00a0 Proven track record of delivering high-quality software solutions in a fast-paced multidisciplinary environment\u00a0 4+ years' experience building secure, performant and well architected applications that demonstrate a focus on data driven iteration and improvement.\u00a0 Preferred Qualifications: Bachelor\u2019s degree in computer science, or related technical discipline AND 6+ years technical engineering experience building applications including, but not limited to, Python, C#, C++, .NET, XAML, Kotlin, Swift OR equivalent experience.\u00a0\u00a0 Experience working with Windows debugging tools such as WinDBG, Sysinternals, etc.\u00a0 Experience working with AI platforms, frameworks, and APIs.\u00a0 Ability to identify, analyze, and resolve complex technical issues, ensuring optimal performance, scalability, and user experience.\u00a0 Dedication to writing clean, maintainable, and well-documented code with a focus on application quality, performance, and security.\u00a0 Demonstrated interpersonal skills and ability to work closely with cross-functional teams, including product managers, designers, and other engineers, while clearly communicating complex technical concepts to both technical and non-technical stakeholders.\u00a0 Ability to work in a fast-paced environment, manage multiple priorities, and adapt to changing requirements and deadlines, while collaborating and contributing to a positive, inclusive work environment that fosters knowledge sharing and growth within the team. Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $117,200 - $229,200 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $153,600 - $250,200 per year. Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $137,600 - $267,000 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $180,400 - $294,000 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay\u00a0\u00a0\u00a0 Microsoft will accept applications and processes offers for these roles on an ongoing basis. #MicrosoftAI #Copilot", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://microsoftai.eightfold.ai/careers/job/1970324837140210", "isPrivate": false}, {"id": 1970324837140026, "name": "Member of Technical Staff- Machine Learning Operations Engineer", "location": "Mountain View, California, United States", "locations": ["Mountain View, California, United States"], "hot": 0, "department": "Copilot (P0PS)", "business_unit": "Microsoft AI (QIA5)", "t_update": 1761742161, "t_create": 1761076380, "ats_job_id": 1970324837140026, "display_job_id": 1970324837140026, "type": "ATS", "id_locale": "1970324837140026-en", "job_description": "Overview: At Microsoft Copilot, we focus on building the best AI powered products in the world. We're building applied AI products designed to improve over time and we need someone to architect and build the infrastructure that makes that possible. As an  Machine Learning Operations (MLOps) Engineer , you'll build the connective tissue between our models and the real world. You're not just deploying models - you're building the systems that accelerates model improvement and drives continuous learning from production. This is a high-impact, high-autonomy role where your infrastructure decisions directly shape product quality and our ability to iterate. If you've ever felt frustrated by the gap between ML's potential and its messy reality in production, this is your chance to close it. The next wave of AI products won't win on model architecture alone\u2014they'll win with robust infrastructure for continuous improvement. You'll build the infrastructure that makes our AI products genuinely intelligent, not just generative. Every system you create shortens the loop between user feedback and model improvement, directly impacting product quality and user experience. Microsoft\u2019s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. 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. This expectation is subject to local law and may vary by jurisdiction. Responsibilities / What you'll build: Training pipelines that scale elegantly - Design and implement robust training infrastructure that handles everything from data ingestion to model versioning, making it trivial for ML engineers to experiment and deploy with confidence The data flywheel - Build the infrastructure and product features that capture user interactions, ground truth labels, and edge cases, then automatically route them back into training loops. Turn every production interaction into a training example Inference systems that deliver - Dive deep into model serving architecture\u2014optimize latency, manage costs, implement intelligent caching, and build the observability needed to maintain reliability at scale Deployment pipelines with guardrails - Create deployment systems that balance velocity with safety: automated testing, gradual rollouts, performance monitoring, and quick rollback mechanisms Cross-functional infrastructure - Partner closely with ML engineers, platform engineers, and data scientists to build APIs and tools that enable tight, rapid feedback loops from production back to model development Required Qualifications:  Doctorate in Computer Science, Statistics, Software Engineering, or related field AND 3+ year(s) applied ML engineering experience OR Master's Degree in Computer Science, Statistics, Software Engineering, or related field AND 4+ years applied ML engineering experience OR Bachelor's Degree in Computer Science, Data Engineering, Software Engineering, or related field AND 6+ years applied ML experience, OR equivalent experience. 6+ years experience building and operating ML systems in production, with real stories about what breaks at scale and how you fixed it 5+ years of experience of software engineering fundamentals with experience in distributed systems, containerization (Docker/Kubernetes), and cloud platforms (AWS/GCP/Azure) 5+ years of hands-on experience with ML orchestration tools (Airflow, Kubeflow, Metaflow), experiment tracking, model registries, and feature stores 5+ years of experience optimizing model inference, wrestled with GPU utilization, and know the tradeoffs between latency, throughput, and cost Preferred Qualifications: Doctorate in Computer Science, Data Engineering, Software Engineering, or related field AND 6+ years data engineering experience (e.g., building ETL pipelines, managing distributed data systems, implementing data quality frameworks) OR Master's Degree in Computer Science, Data Engineering, Software Engineering, or related field AND 8+ years data engineering experience (e.g., building ETL pipelines, managing distributed data systems, implementing data quality frameworks) OR Bachelor's Degree in Computer Science, Data Engineering, Software Engineering, or related field AND 10+ years data engineering experience (e.g., building ETL pipelines, managing distributed data systems, implementing data quality frameworks) OR equivalent experience. Familiarity with LLM deployment patterns, vector databases, prompt management, and the unique challenges of serving foundation models Experience working with RAG, fine-tuning pipelines, or evaluation frameworks The ability to see beyond individual components to design holistic systems where data flows naturally from production through improvement cycles and back Desire and preference to work at the intersection of teams, translating between ML researchers who want flexibility and engineers who need reliability Data Science IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year.\u00a0There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $158,400 - $258,000 per year. Data Science IC5 - The typical base pay range for this role across the U.S. is USD $139,900 - $274,800 per year.\u00a0There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year. Certain roles may be eligible for benefits and other compensation. 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