Pear VC
Chief of Staff - NomadicML
San Francisco · Exec
Sponsorship not specifiedDetected 162 days ago
Distributed SystemsSnowflakeMachine LearningProduct ManagementProduct StrategyOKRsRoboticsResearchCommunication
About the role
- NomadicML is entering a phase of rapid growth-expanding our partnerships with top autonomy companies and accelerating our research into video-reasoning models.
- We're seeking a Chief of Staff to work directly with the founders on the company's most critical strategic, operational, and communication initiatives.
- You'll act as a connector between research, engineering, operations, and external stakeholders, ensuring that our product roadmap, business goals, and partnerships stay tightly aligned.
Responsibilities
- Partner directly with the founders to manage key priorities, company goals, and investor initiatives.
- Drive internal communication and alignment, ensuring all teams understand what's most important week-to-week.
- Lead special projects spanning GTM strategy, hiring, partnerships, and research collaboration.
- Support fundraising and investor relations, including data gathering, memo writing, and due-diligence coordination.
- Own cross-functional initiatives such as launch plans, product updates, and key customer deliverables.
Requirements
- you can simplify chaos into a clear plan.
- Exceptional writing and communication skills-you can draft crisp memos, investor updates, and presentations.
Nice to have
- Prior experience in AI / ML, autonomous systems, or deep-tech startups.
- Experience with technical products, VC communications, or enterprise partnerships.
- Comfort working closely with technical founders and interpreting complex technical concepts for business audiences.
Benefits
- Deep curiosity about AI, machine learning, or technical products-you're excited to learn how things actually work.
Apply directly at Pear VC →Create a free account for alerts like thisView Pear VC immigration profile
This listing is sourced directly from Pear VC's careers page and normalized into a canonical job model.