Chan Zuckerberg Initiative

Exempt Organizations Counsel, Legal

Redwood City, CA (Remote) · Staff+

Sponsorship not specified$214k-$298kDetected 26 days ago

Project ManagementCompensationWordPressResearchCommunicationCollaborationOrganizational Skills

About the role

If you are a team player who enjoys solving nuanced and varied legal issues, and doesn't mind rolling up your sleeves and diving into new projects, then we're looking forward to hearing from you!

Review, draft, and negotiate a broad range of agreements and related documents, partnering closely with program colleagues on grantmaking, impact investing, and other funding and collaboration activities.
Draft and implement clear and concise policies, practices, and other resources needed to establish and scale compliance for our tax-exempt entities.
Establish, implement, and improve our client service-oriented approach to ongoing inquiries about tax exempt-specific legal rules and processes.
Develop and deliver trainings to key stakeholders at all levels across the organization.
Provides a generous employer match on employee 401(k) contributions to support planning for the future.
Relocation support for employees who need assistance moving

JD required and an active membership in at least one U.S. state bar, preferably California.
7+ years of legal experience with at least 3 years of experience handling legal issues associated with complex philanthropic organizations.

Your guidance will be critical to furthering programmatic strategies and operational goals, and will span all aspects of our work.

The Redwood City, CA base pay range for a new hire in this role is $214,000 - $298,000.
New hires are typically hired into the lower portion of the range, enabling employee growth in the range over time.
Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process.
Pay ranges outside Redwood City, CA are adjusted based on cost of labor in each respective geographical market. Your recruiter can share more about the specific pay range for your location during the hiring process.
This position may be eligible to participate in CZI's discretionary annual performance bonus program. Bonus eligibility and targets are determined in accordance with CZI's total rewards philosophy and may vary by role.
Better Together

Paid time off to volunteer at an organization of your choice.
Funding for select family-forming benefits.
Demonstrated interest in science, artificial intelligence, and / or education.
Benefits for the Whole You
To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible.

Our Legal and Grants team ("Team LeGiT") is a key partner in enabling Biohub' s pioneering work to combine frontier AI with frontier biology to prevent and cure disease and Learning Commons ' mission to build AI infrastructure that aligns how students learn with the tools that shape their learning experiences.
Team LeGiT helps accelerate Biohub's and Learning Commons' missions by structuring, negotiating and executing on strategic partnerships, grants and contracts, as well as providing advice that spans IP, technology, exempt org and labor law.
We work directly with scientists, engineers and educators in the development of frontier AI models, partnerships with universities, and a wide range of science and education tools and products.
In addition to supporting our programmatic colleagues, we regularly collaborate across Executive, Operations, Central Tech and Investments teams to advance the organization's philanthropic goals.
Our team also manages Mark and Priscilla's Family Giving to provide funding for nonprofit organizations, including in the communities where they live and work.
At its core, this team exists to build trust - with our partners, our grantees, and the communities we serve - and to help our organizations operate with integrity as we take on some of the world's most complex challenges.

This listing is sourced directly from Chan Zuckerberg Initiative's careers page and normalized into a canonical job model.