Engineering Manager - Machine Learning Core Environment - Cortex
Who We Are:
Cortex enables, accelerates and grows machine learning at Twitter. The Machine Learning Core Environment team has the following mission: Enable high-quality ML model development and deployment by building and maintaining an open, flexible and productionizable AI environment.
In practice, the ML Core Environment team implements and maintains services that allow the whole company to run their models using a unified API, and creates and maintains libraries that make it easy to train, deploy and run models from within the Twitter infrastructure. By doing so, the team can improve the velocity of machine learning iteration and standardize, for example, observability for machine learning, online learning or distributed graph computation.
As such, a focus on correct design abstractions, API stability over time and exceptional developer experience is of paramount importance. This is a challenging and exciting area, with a unique opportunity to create advanced technologies in uncharted territory while having a large, near-term impact.
What You’ll Do:
You will lead diverse, smart, and driven engineers and align their career ambitions with business requirements and opportunities. We're looking for a leader who can communicate fearlessly to build trust and take an active role in shaping the future of Twitter engineering while embodying our core values. As a Manager for Machine Learning Core Environment you will:
Manage a portfolio of projects, balancing long-term innovative bets with a regular delivery cadence of customer value
- Enroll customers into co-developing and adopting platform solutions
- Coordinate and align with other teams in Cortex and throughout the company to deliver re-usable solutions and leverage prior work
- Strive for technical excellence and longevity of solutions
- Guide and mentor your team members in their professional and personal development
- Build strong relationships with team members, customers and partners based on trust and mutual respect
- Look ahead to identify opportunities and mitigate risks
- Communicate expectations, feedback and context to team members, partners and stakeholders early and often, succinctly and with class
- Seek diverse perspectives to drive innovation and consensus from all technical partners inside and outside the team.
- Be a source of engineering talent and be comfortable closing candidates.
- Be a strategic leader in the Cortex organization
- Twitter deep learning stack
- Machine Learning observability
- Centralized, services for ML models runtime
- Generic runtime engine for ML models in the Twitter Stack
- Distributed computational graphs
Who You Are:
You have a passion for machine learning and improving the ways people consume the world in real-time. You are excited to join an incredibly talented team which loves to take on new challenges. You like a fast-paced & fun environment, believe in Twitter’s mission in the world and want to be a core actor in pushing it forward.
Knowledge and experience with techniques, frameworks and processes used in Machine Learning, data engineering
Experience in building developer-focused API’s or platforms
Knowledge and experience with large scale distributed systems
Customer-driven approach to software
Excellent communication skills
Experience in mentoring and growing strong engineers
Large team technical leadership experience or management experience
Machine learning experience
Experience with open source contributions
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.
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 come onsite 1-2 times to interview with a total of 5-10 people.
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