Overview
Microsoft is a company where passionate innovators come to collaborate, envision what can be and take their careers further. This is a world of more possibilities, more innovation, more openness, and the sky is the limit thinking in a cloud-enabled world. The AI Platform organization at Microsoft builds the end-to-end Azure AI stack/PaaS and is core to Azure's innovation and differentiation, as well as all of Microsoft's flagship products, from Office to Teams, to Xbox. We are the team building Azure OpenAI, Azure ML, Cognitive Services, and the global Azure AI infrastructure for running the largest AI workloads on the planet. Within AI Platform, our team in Evaluation AI is working on cutting-edge NLP and Deep Learning models and building the next generation model evaluation platform. We are looking for a passionate, creative, analytical Machine Learning Engineer who loves NLP, deep learning and wants to ship products quickly at a massive scale. We will provide a lot of opportunities for you to learn, grow and contribute. We do not just value differences or different perspectives. We seek them out and invite them in so we can tap into the collective power of everyone in the company. As a result, our customers are better served. Microsoft's 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.
Responsibilities
As a Machine Learning Engineer, you will:
- Work on architecture, design and development of the next generation of Azure AI's evaluation platform.
- Work with other researchers, applied scientists, and machine learning engineers in the team to design and build the end-to-end pipelines covering model training, data analysis, model serving and model evaluation.
- Implement latest evaluation methods from published literature and methods in the industry.
- Drive new product features and evaluation metrics.
- Embody our culture and values.