Juniper Ventures

This stream focuses on building companies in AI assurance infrastructure, from energy and physical networking to authentication and interpretability.

Stream overview

Do you have deep domain knowledge, an urgency for action, and an idea that seems incredibly obvious but you haven’t seen anyone build it? We want to mentor founders / founding researchers who are incredibly ambitious about technical AI safety. You will join the likes of Goodfire, AIUC, and others to build the technologies necessary to make AI beneficial for humanity.

We’re into assurance infrascructure like energy, hardware, physical networking (hard), compute, security, audits (cloud), data, evals, interpretability (model), inference, guardrails, observability (runtime), authentication, agents, integrations (distribution), compliance, insurance, policy (governance).

Mentorship style:

Standard (1-2 hours of weekly 1:1s)

Location during program:

SF Bay Area

London location preference:

No preference for this location

Berkeley location preference:

Strong preference

Mentors

Nick Fitz (Nick)
Juniper Ventures
,
Founder
Founding and Field-Building

Nick Fitz is a founder and general partner at Juniper Ventures. He previously co-founded and led Momentum, an AI donor-engagement company acquired by Virtuous in 2025.

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Griff Bohm
Juniper Ventures
,
Founding Managing Partner
Founding and Field-Building

I have founded three successful businesses and sold two, and now I've turned to investing in AI Safety.

Over the years I've learned a lot about what it takes to actually manage and run an organization, focus on what matters and scale the things that are important.

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Esben Kran
Juniper Ventures
,
Platform Partner
No items found.

Esben founded Apart Research, an AI safety research organization where he now serves as board chair, and Seldon Labs, an accelerator for AI safety startups. He co-founded ENAIS, the European Network for AI Safety, and built DarkBench, a benchmark for manipulative behaviors in language models. A serial entrepreneur trained in cognitive science, his work spans evals, interpretability, and turning research into assurance.

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Fellows we are looking for

Strong candidates range from successful empirical ML researchers, who want to take their research into the real world, to people with a founding engineering or security background.

Project selection

Scholars either propose a project or shape one with us from a shortlist of open problems drawn from what we see across the assurance landscape. We scope the work together early to achieve the most progress during the program.