Commercial Counsel - US (Remote)

Posted 23 Days Ago
Hiring Remotely in San Francisco, CA
Remote
148K-203K Annually
3-5 Years Experience
Machine Learning • Software
The Role
The Commercial Counsel will negotiate SaaS and software licensing agreements, vendor contracts, and various commercial agreements while working closely with internal teams. They will also develop legal materials and training, and improve policies and processes for managing legal risk.
Summary Generated by Built In

At Weights & Biases, our mission is to build the best tools for AI developers. We founded our company on the insight that while there were excellent tools for developers to build better code, there were no similarly great tools to help ML practitioners build better models. Starting with our first experiment tracking product, we have since expanded our solution into a comprehensive AI developer platform for organizations focused on building their own deep learning models and generative AI applications.


Weights & Biases is a Series C company with $250M in funding and over 200 employees. We proudly serve over 1,000 customers and more than 30 foundation model builders including customers such as OpenAI, NVIDIA, Microsoft, and Toyota.


Reporting to the Managing Counsel, the Commercial Counsel is a strategically important role that will support Weights & Biases at a time of rapid growth. The Commercial Counsel will partner closely with internal business teams on a broad variety of transactional and other business matters.

Responsibilities

  • Review and negotiate SaaS and software licensing agreements with enterprise customers, working closely with Sales, Finance and Product to drive deals to closure
  • Review and negotiate a wide range of other commercial agreements, including DPAs and partnership agreements, working with stakeholders across the organization
  • Review and negotiate a wide range of vendor contracts; develop and update vendor contract templates, policies and playbook; promote good contract management practices throughout the company;
  • Develop Legal enablement materials and conduct trainings for the GTM team
  • Implement and improve policies and processes to manage legal and business risk on a global scale

Requirements

  • JD and a member of a U.S. state Bar in good standing
  • 4+ years of combined experience in-house and at a law firm, including commercial and tech transactions experience, preferably at a VC-backed technology company
  • This role is required to work in PST hours
  • Substantive knowledge of SaaS and software licensing
  • Knowledge of GDPR and Privacy Law an advantage
  • A track record of closing challenging deals
  • Practical, solution-oriented and ready to roll up your sleeves
  • Excellent communication skills with the ability to translate complex legal concepts into easy-to-understand advice
  • Interest in machine learning technology and applications

Our Benefits

  • 🏝️ Flexible time off
  • 🩺 Medical, Dental, and Vision for employees and Family Coverage
  • 🏠 Remote first culture with in-office flexibility in San Francisco
  • 💵 Home office budget with a new high-powered laptop
  • 🥇 Truly competitive salary and equity
  • 🚼 12 weeks of Parental leave (U.S. specific)
  • 📈 401(k) (U.S. specific)
  • Supplemental benefits may be available depending on your location
  • Explore benefits by country

We encourage you to apply even if your experience doesn't perfectly align with the job description as we seek out diverse and creative perspectives. Team members who love to learn and collaborate in an inclusive environment will flourish with us. We are an equal opportunity employer and do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. If you need additional accommodations to feel comfortable during your interview process, reach out at [email protected].


#LI-Remote

The Company
HQ: San Francisco, CA
132 Employees
On-site Workplace
Year Founded: 2017

What We Do

Weights & Biases helps machine learning teams build better models faster. With a few lines of code, practitioners can instantly debug, compare and reproduce their models — architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions — and collaborate with their teammates.

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