.png)
Lithos builds the fastest agentic inference on the planet: frontier open models at full precision on standard GPUs. Our engine currently holds top spots on Artificial Analysis for output speed, time to first token and end-to-end latency on models like DeepSeek and Kimi. We serve inference to growing startups as well as FT500 companies through a public API or on their own hardware.
Lithos was founded by Carnegie Mellon computer science professors who spent years turning systems research on inference and GPU virtualization into a product. We are a small team, and this is one of our first ten hires.
Why this role matters
We have a product that is measurably faster than the alternatives and customers who care about speed. What we don't have yet is a clear story, a known audience and a steady presence in the places AI developers pay attention.
You will be our first marketer. This is a hands-on role: you will set the strategy and do the work yourself, from positioning to the first blog post. We need someone who can figure out who our buyers are, decide what we should say to them, and get our founders in front of the right people. What you build becomes the foundation for the rest of our marketing.
What you'll do
Own positioning and messaging. Define how Lithos is described to AI developers and engineering leaders building agents and customer-facing AI features where speed matters. Turn benchmark results and customer wins into a story people remember.
Find our audience. Work out who our best buyers are, where they spend their time and what makes them try something new. Use what you learn to focus everything else.
Create the content that earns developer trust. Technical blog posts, benchmark write-ups, launch announcements, docs pages, case studies and customer videos, written or shaped by you.
Raise the profile of Lithos and its founders. Get the founders into the right conferences, podcasts, panels and communities, and help them show up prepared, with talks and posts that land.
Run launches and events. Plan and ship product launches, and work with partners and investors on developer meetups and executive dinners.
Arm the people who sell. Build the pitch, website copy, competitive positioning and customer-facing materials the founders and our founding account executive use in every deal.
Measure what works. Track which channels and messages bring in real usage and pipeline, and put more into what works.
What we're looking for
4+ years in product marketing for a technical product sold to developers or engineering teams, ideally in AI infrastructure, cloud, developer platforms or data infrastructure.
Real technical depth. You can explain latency, throughput and cost per token, read our docs, run a quick benchmark yourself, and hold your own with engineers.
Experience at an early-stage startup, where you built the marketing function or a big part of it instead of inheriting one.
Your own point of view. You set direction rather than waiting for instructions, and you are comfortable being the only marketer for a while.
Excellent writing. You can make a technical idea clear, specific and interesting in a few sentences.
You want to stay hands-on and build, not manage a team right away.
Nice to have: you have marketed inference, GPU cloud, model APIs or agent tooling, launched an open-source or developer-first product, or have an engineering background.
Unusual Ventures Menlo Park, California, USA Office
200 Middlefield Rd, Menlo Park, CA, United States, 94025
Similar Jobs
What you need to know about the San Francisco Tech Scene
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
