Machine Learning Research Engineer - Cortex Applied Machine Learning

San Francisco, CA


Who we are:

We are a community of Machine Learning Researchers and Engineers, working to improve Twitter through applications of ML through a range of systems - e.g. recommendations, safety, abuse, ads. We operate at scale whilst ensuring fair and ethical use of our models and data.

We work as embedded researchers amongst product teams, looking to apply the expertise of the individuals to improve our products and unlock new capabilities.

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 optimise the systems we already. 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 ML-driven products.

Who you are:

You have a depths of knowledge in a ML-driven field - e.g. NLP, Computer Vision, Prediction/Inference, etc and you are interested in applying your knowledge and skill set to one or more of our product areas - e.g. media / content understanding, behavioural understanding, recommendation systems, 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.

Requirements:

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, 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, CVPR, ICCV, ECCV, etc

Experience with one or more of the following:

Approximate / k-nearest neighbour theory, algorithms and frameworks

Natural Language Processing

Recommender Systems

Model optimisation

Prediction / Inference (e.g. Bayesian)

Online Learning

Reinforcement Learning


Equal Opportunity Statement

We are committed to an inclusive and diverse Twitter. Twitter is an equal opportunity employer. We do not discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status or any legally protected status.




Hiring Process

Step 1

After you apply, a recruiter may reach out to you for an introductory call.

Step 2

If your background is a match for the role, you may phone interview with 1-2 people.

Step 3

If you continue through the process, you will come onsite 1-2 times to interview with a total of 5-10 people.

Application

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