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 in product areas such as recommendations, safety, abuse, ads. We operate at scale whilst ensuring fair and ethical use of our models and data.

We work as a team of specialists embedded within product teams, looking to apply the expertise of the individuals, and working with product teams to improve our products and unlock new capabilities.

What you will do:

Apply your research and engineering skills to either improve existing solutions, unlock new directions or provide entirely new solutions within Twitter. You will work closely with live production systems and product teams, and learn to deliver ML solutions at scale within the Twitter tech stack, whilst encouraging best practices for ML across the company.

Who you are:

You are interested in a range of applications of ML which could stem from media understanding to recommendation systems. You are passionate about the way we develop state-of-the-art technologies, how we apply them to products, and enjoy the challenge of overcoming real-world constraints. 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:

Masters’ or PhD in a Computer Science or Machine Learning related degree; or equivalent work experience in the field

Good theoretical grounding in core Machine Learning concepts and techniques

Ability to perform literature reviews and provide feedback on state-of-the-art solutions and how they may fit to different operating constraints

Practitioner of modern software development practices - e.g. TDD, CI, source control

Experienced with modern ML techniques - particularly deep learning models and architectures - such as CNNs, RNNs, LSTMs

1+ years experience with one or more DL software frameworks such as Tensorflow, PyTorch

3+ years software engineering experience

Nice to haves:

Experience with large-scale systems and data, e.g. Hadoop, distributed systems

Experience with one or more of the following:

Approximate / k Nearest Neighbour theory, algorithms and frameworks

Recommender Systems

Model optimisation and parameter selection

Online Learning

Reinforcement Learning

Publications in top conferences such as ICLR, NIPS, ICML, CVPR, ICCV, ECCV, etc



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.



Engineering Hiring Process

Step 1

Once your application is received, a recruiter will reach out pending your qualifications are a match for the role.

Step 2

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.

Step 3

If the phone interviews go well or your work sample is strong, the final step includes interviews with 5-6 people held onsite in our office.

Application

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