Machine Learning Researcher, Applied Machine Learning Team
Vimeo is building a team of Machine Learning Researchers and Engineers with strong bias of action. ML Researchers will work closely with Applied Machine learning and bring innovative products to our vibrant creator community.
Apply your research expertise to either improve existing solutions, unlock new opportunities, or explore and design entirely new solutions to solve challenging problems within Vimeo.
You’re a machine learning researcher with interests in a range of applications which could stem from Video to recommendation systems. You are passionate about the way we develop state-of-the-art technologies and apply them to products. You keep up to date with the latest developments in the field and look for ways to apply them to your current work/role. You stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.
- Develop solutions for real world, large scale problems, including but not limited to Video tech, infrastructure, Security, Legal (NSFW, Spam), SEO, Marketing and Creator Products.
- Conduct cutting edge research in machine intelligence and machine learning applications.
- Actively contribute to the wider research community by partnering with universities and publishing papers.
- Experience with large-scale systems and data, e.g. Hadoop, distributed systems
- Relevant work experience, including experience working within the industry or as a researcher in a lab, with one or more of the following:
- Machine Learning and Neural Networks
- Recommendation Systems
- Model Optimisation and Fine Tuning - both training and runtime
- Online Learning
- Reinforcement Learning
- Publications in top conferences such as ICLR, NIPS, ICML, CVPR, ICCV, ECCV, KDD, IEEE, etc
- Expertise in applying Optimization Models, and a good theoretical grounding in core Machine Learning concepts and techniques
- Experience with a number of ML techniques and frameworks, e.g. data discretization, normalization, sampling, linear regression, decision trees, SVMs, deep neural networks, etc
- Ability to perform comprehensive literature reviews and provide critical feedback on state-of-the-art solutions and how they may fit to different operating constraints with the ability to design and execute on research agenda.
- Familiarity with one or more DL software frameworks such as Tensorflow, PyTorch
- Strong Bias for Action
- Post-graduate or PhD or equivalent work experience in computer science, machine learning, information retrieval, recommendation systems, natural language processing, statistics, math, engineering, operations research, or other quantitative discipline