Heather Diles is senior counsel in Wilson Sonsini Goodrich & Rosati’s employment law practice. Heather represents companies in federal and state court litigation and before administrative agencies regarding discrimination (including race, sex, age, national origin, sexual orientation, and disability), harassment, retaliation, breach of contract, wage and hour, wrongful termination, and numerous other types of employment law matters.
In addition to her litigation practice, Heather regularly advises employers regarding all aspects of the employment relationship, including preventive personnel practices, enforceability of covenants not to compete, hiring practices, employee classification issues, employment contracts, wage and hour matters, independent contractor audits, family and medical leaves, workplace investigations, terminations, and reductions in force.
Further, Heather also assists clients in merger and acquisitions by advising on structuring corporate transactions to minimize employment-related risks and maximize the value of the transaction from an employment perspective. Her merger and acquisition experience consists of representing both buyers and sellers in large and small transactions, including in mergers, acquisitions, IPOs, asset purchases, and financing.
Prior to joining the firm, Heather was an associate at Simpson Thacher & Bartlett LLP in New York.
Heather Diles is senior counsel in Wilson Sonsini Goodrich & Rosati’s employment law practice. Heather represents companies in federal and state court litigation and before administrative agencies regarding discrimination (including race, sex, age, national origin, sexual orientation, and disability), harassment, retaliation, breach of contract, wage and hour, wrongful termination, and numerous other types of employment law matters.
In addition to her litigation practice, Heather regularly advises employers regarding all aspects of the employment relationship, including preventive personnel practices, enforceability of covenants not to compete, hiring practices, employee classification issues, employment contracts, wage and hour matters, independent contractor audits, family and medical leaves, workplace investigations, terminations, and reductions in force.
Further, Heather also assists clients in merger and acquisitions by advising on structuring corporate transactions to minimize employment-related risks and maximize the value of the transaction from an employment perspective. Her merger and acquisition experience consists of representing both buyers and sellers in large and small transactions, including in mergers, acquisitions, IPOs, asset purchases, and financing.
Prior to joining the firm, Heather was an associate at Simpson Thacher & Bartlett LLP in New York.
- J.D., New York University School of Law, 2017Order of the Coif, Magna Cum Laude, Florence Allen Scholar
- B.A., Sociology and Psychology, Bucknell University, 2014Summa Cum Laude, Phi Beta Kappa, Presidential Fellow
- State Bar of New York
- U.S. District Court for the Eastern District of New York
- U.S. District Court for the Southern District of New York
- J.D., New York University School of Law, 2017Order of the Coif, Magna Cum Laude, Florence Allen Scholar
- B.A., Sociology and Psychology, Bucknell University, 2014Summa Cum Laude, Phi Beta Kappa, Presidential Fellow
- State Bar of New York
- U.S. District Court for the Eastern District of New York
- U.S. District Court for the Southern District of New York
As described by Foundry, the acquisition accelerates Foundry's AI strategy, adding critical orchestration capabilities to complement the AI-powered features in Foundry's product portfolio. It represents the next evolution of this strategy, enabling studios to orchestrate multiple AI models and agents within secure, professional workflows that connect seamlessly with Foundry's existing creative toolsets. As studios shift from experimentation to everyday production use, the need for better control of AI models has become critical. Griptape enables controlled access to rapidly evolving open-source and commercial AI models in a user-friendly framework, while balancing the strict security and traceability required by major production environments.
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