AudaciousAgentic CRM for forward-deployed companies.
Lightfield is an AI-native CRM that assembles itself from your email, calendar, and meetings. It captures every interaction and turns it into organized context, accounts, tasks, follow-ups, and insights, so nothing slips through the cracks. Instead of forcing teams to maintain rigid systems, Lightfield learns from how companies actually work, adapting, automating, and surfacing the insight that drives growth, the CRM they always wished existed: fast, intelligent, and genuinely helpful.
Keith Peiris
Previously co-founded Tome, a generative AI presentation product used by over 25 million people.
Henri Liriani
Previously co-founded Tome, a generative AI presentation product used by over 25 million people.
Pete Nichols
Previously VP Engineering at Tome, SVP of Engineering at Divvy Homes, and Head of Pinterest’s Developer Platform.
Deep product pedigree
Before Lightfield, the team worked on Llama, Instagram, Facebook Messenger, Pinterest, Google, and Salesforce.
See Lightfield in action.
CRM, rethought from first principles.
Lightfield has shipped a product founders love, with strong product-led traction in self-serve since launching in November. Now they’re building out the platform, the AI systems that turn every interaction into structured context, fast, intelligent, and genuinely helpful.
A CRM that assembles itself
It builds from your email, calendar, and meetings into accounts, tasks, follow-ups, and insights, so nothing slips through the cracks.
Founder-loved, growing fast
Strong product-led growth in self-serve since November, with team adoption, multi-seat usage, and CRM-migration signals across the base.
Meaty engineering, close to users
Assembling a CRM from unstructured email, calendar, and meetings is a hard systems-plus-AI problem, with real users depending on it being right.
Backed by Tier 1 investors.
A ~$50M Series A from Tier 1 investors. The founding team previously built Tome (25M+ users).
We don’t just build features. We solve problems end to end.
At Lightfield, engineers are owners. They work in autonomous pods to solve real problems from start to finish, building agents that deliver impact.
Identify the problem
Deeply understand the core issue and the user impact.
Develop the solution
Design the strategy, architecture, and agent that will solve it.
Execute the solution
Implement, test, iterate, and follow through to completion.
We work in pods
Cross-functional teams of engineers and designers building agents together.
We build agents
Products powered by intelligent agents that solve complex problems.
Move fast or go deep
Some projects take a week, others 3–4 months. Impact over process.
You own it all
No product managers, just designers. You own the entire thing from end to end.
Full autonomy. Real ownership. Massive impact. At Lightfield, you don’t just ship code, you solve meaningful problems and build what matters.
What they’re hiring for.
Lightfield is hiring three Staff-level engineers, AI/ML first, then backend / infrastructure, then a generalist full-stack builder. In office four days a week, work-from-home Wednesdays. Small team, huge surface area, and real users from day one.
Staff AI/ML Product Engineer
- Build AI products end to end, lead projects, and mentor earlier-career engineers.
- Own the LLM systems that turn email, calendar, and meetings into accurate, trustworthy CRM context, extraction, retrieval, and evals.
Staff Backend / Infrastructure Engineer
- Operate complex backend services at scale, capacity planning, performance bottlenecks, and scaling as more customers onboard.
- Own system design for complex backend features, RBAC, enrichment workflows, and automations, making the right architectural calls in the v1 build.
Staff Full-Stack Engineer
- A generalist builder who works vertically across the stack, shipping product surface and services end to end.
- Thrives on zero-to-one work in a fast-changing environment without strict specialization.
A lot of what we’re looking for here is really backend more than pure infra, with a baseline of product and commercial orientation. A strong candidate brings one or both:
- Experience operating complex backend services at significant scale, capacity planning, performance bottlenecks, and scaling as we onboard more customers.
- Strong system design for complex backend product features like RBAC, enrichment workflows, and automations. A current bottleneck is getting the right design choices made across projects in the v1 implementation, so experienced coverage here goes a long way.
Less of a fit: pure ML infra (we don’t run our own models or self-host inference in production, though backend-scalability experience there can still be interesting), Developer Efficiency / DevEx, or a full-time DevOps / Kubernetes / AWS / Terraform role, that work is useful, but everyone contributes meaningfully to backend product, and the most experienced hires should be among the strongest contributors there.
What stands out
- Impressive, tangible outcomes, ideally with a clear throughline to product or commercial impact.
- Evidence of solving genuinely difficult backend problems, with concrete signals of complexity: scale, latency, throughput, availability, data volume, correctness, or architectural complexity.
Who thrives: super-IC builders more than managers, people who stay hands-on but can flex into tech lead or light management when needed. Zero-to-one types who work vertically across the stack and are comfortable in a fast-changing environment without strict specialization.
Early, and moving incredibly fast
A small team scaling quickly, where every engineer shapes the core product and owns real surface area from day one.
Build the CRM that builds itself.
A founder-loved, AI-native CRM with a ~$50M Series A from Tier 1 investors, by the team that built Tome (25M+ users). Early engineers own the product surface and the AI systems underneath.