Who we Are:
Cortex empowers internal teams to efficiently leverage ML by providing a platform and by unifying, educating, and advancing the state of the art in ML technologies within Twitter. We win when our customers (Twitter ML Engineers, Practitioners, and internal stakeholders) win by helping Twitter’s users stay informed, share and discuss what matters; by serving the healthy public conversation. We’re building an AI-first company and every major initiative is increasingly dependent on the successful application of machine learning. Cortex is at the nexus of this evolution.
Our team of ML software engineers are constructing one of the strongest machine learning platforms in the world by marrying the latest ML industry practices with engineering excellence and the need to perform at Twitter scale. Our goal is to provide tooling that allows Twitter engineers to focus on what they are good at, building ML models, and abstract the way the complexities of experimenting with and bringing these models into a production environment with novel state of the art approaches.
What You’ll Do:
We look for someone who can lead a team of diverse, smart and driven engineers distributed across multiple geos. We need leaders who take an active role in shaping the future of Twitter engineering while embodying our core values. A successful engineering manager will:
Lead a team of talented machine learning software engineers who like to ship code and tackle hard engineering problems.
Build cohesive, high-functioning teams that thrive in a culture of trust, respect, and inclusion.
Balance autonomy with guidance by giving your team the tools, context, confidence, and motivation to make decisions effectively and independently.
Have the technical capacity to partner with tech leads and be comfortable diving into the fray to help drive resolution in the case of incidents.
Take responsibility for the team’s short-term and long-term strategy. Define the team's roadmap, success metrics, and priorities in close collaboration with other engineering teams and multi-functional partners.
Maintain a balance between building sustainable, high-impact projects and shipping things quickly.
Own your team’s deliverables and ensure we develop scalable, highly-available infrastructure that enables product engineers to experiment and rapidly iterate on products that delight our users and customers.
Be an aggressive source of engineering talent and be comfortable closing high potential candidates from diverse backgrounds.
Augmenting ML Platform capabilities for data preparation, feature extraction, training and analysis with Google Cloud technologies like BigQuery and Dataflow
Developing capabilities to hot swap hundreds of live production models
Model and feature coverage analysis and alerting
Developing ML pipeline solutions using TFX and Kubeflow
Building out Jupyter Notebooks as a first class offering at Twitter that 1) integrates both Twitter and open source APIs, developer tools, and dashboards and 2) is run on both on-prem and cloud based environments.
Working with product teams to ensure ML algorithms are fair and free of unwanted bias.
Working with product teams to enable efficiency improvements across their ML related tasks.