Staff Machine Learning Researcher / Research Engineer - Cortex Recommender Systems Research
San Francisco, Seattle, Boulder
At Twitter, we would like to connect people with the conversations, topics and content that are most relevant to them, in real-time.
We are a community of Machine Learning Researchers and Engineers, working to drive Twitter's research in recommender systems. We tackle local and global technical challenges amongst product teams through a range of systems - e.g. timelines ranking, push notifications, email notifications and ads predictions. We operate at scale whilst ensuring fair and ethical use of our models and data.
Apply your research expertise to improve our ML-driven recommender system products, help us develop new solutions and unlock new directions, as well as analyse and optimise the systems we already have. You'll work closely with product teams and mentor them on best practices for modern ML, and keep the wider team informed on the state-of-the-art. In addition, you will be in a strategic position to influence future roadmaps for Twitter's recommender system products.
Who you are:
You have a depth of knowledge in a ML-driven field - e.g. Probabilistic modeling, Reinforcement learning, Deep learning etc and you are interested in applying your knowledge and skill set to one or more challenges of our product areas - e.g. media / content understanding, new item/user modeling, temporal modeling, model performance optimisation. You are passionate about the way we develop state-of-the-art technologies and are excited by the application of theory to real-world problems. You keep up to date with the latest developments in the field and look for ways to apply them to your current work/role.
Master, Post-graduate or PhD in computer science, machine learning, information retrieval, recommendation systems, natural language processing, statistics, math, engineering, operations research, or other quantitative discipline; or equivalent work experience
Good theoretical grounding in core machine learning concepts and techniques
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.
Experience with a number of ML techniques and frameworks, e.g. data discretization, normalization, sampling, linear regression, decision trees, SVMs, deep neural networks, bandits, reinforcement learning etc
Familiarity with one or more DL software frameworks such as Tensorflow, PyTorch
Nice to haves:
Experience with large-scale systems and data, e.g. Hadoop, distributed systems
Publications in top conferences such as ICLR, NIPS, ICML, RECSYS, CVPR, ICCV, ECCV, etc
Experience with one or more of the following:
Prediction / Inference (e.g. Bayesian)
We are committed to an inclusive and diverse Twitter. Twitter is an equal opportunity employer. We do not discriminate based on race, ethnicity, color, ancestry, national origin, religion, sex, sexual orientation, gender identity, age, disability, veteran status, genetic information, marital status or any other legally protected status.
Engineering hiring process
Once your application is received, a recruiter will reach out pending your qualifications are a match for the role.
If your background is a match, you may have 1-2 technical phone interviews or be given the chance to provide a work sample depending on the role.
If the phone interviews go well or your work sample is strong, the final step includes interviews with 5-6 people via a video conference call.