Machine Learning Research Engineer - Cortex Recommender Systems Research
Who we are
Are you interested in helping build a new Twitter experience? Our team is working to connect users to content based on their interests and intent in addition to who they follow. We do this working within the Twitter Search stack -- it is the search engine for Twitter, and the way to find the most relevant and engaging content for any topic or interest. Twitter Search is a fast growing search engine, which our users rely on to find public accounts and conversations. The product requires building a high quality experience for a wide range of queries in real time. Often this means our systems need to understand the intent behind user queries, be able to match large streams of content with that intent, which are then ranked and displayed. We build technology to enable us to deliver this at scale. We have a mix of Systems and Applied ML engineers who work on the user facing product, as well as the models that improve the quality of the product.
What you will do
Apply your research expertise to improve our ML driven products, help us develop new solutions and unlock new directions, as well as analyse and improve the systems we already have. You’ll work closely with product teams and ML researchers and engineers and mentor them on standard methodologies for modern ML, and keep the wider team informed on the state-of-the-art as well as alternative directions to take. You will lead research projects to enable Twitter to better bring to bear ML on its platforms and advance the state-of-the-art in ML. You will be collaborating on strategic decisions and future roadmaps for Machine Learning driven products and technologies at Twitter. Your impact will directly affect millions of Twitter users around the globe.
Who you are
Apply your research expertise to help direct our product modeling efforts, focusing on using creative data-driven ML approaches to solving open-ended product questions. Help us develop new solutions and unlock new directions, as well as analyze and improve the systems we already have. You’ll work closely with ML engineers and data scientists and mentor them on modern approaches to using ML for solving product challenges at Twitter’s scale. You will be collaborating on strategic decisions and future roadmaps for Machine Learning driven products and technologies at Twitter. You are a key member of the Cortex Boston Product Modeling team, which consists of authorities in ML, data science, and software engineering. Your impact will directly affect millions of Twitter users around the globe.
- 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, NeurIPS, ICML, RECSYS, CVPR, ICCV, ECCV, etc
- Experience with one or more of the following:
- Natural Language Processing
- Recommender Systems
- Model optimisation
- Prediction / Inference (e.g. Bayesian)
- Online Learning
- Reinforcement Learning
Equal opportunity employment
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.
After you apply, a recruiter may reach out to you for an introductory call.
If your background is a match for the role, you may phone interview with 1-2 people.
If you continue through the process, you will interview with 5-10 people via a video conference call.