Founding SDR

Inlet builds automated timekeeping for law firms. Lawyers bill by the hour, but tracking time is the worst part of their job - most lose 10-30% of billable hours simply because they forget to log what they did. We capture and categorize their work automatically so they can focus on practicing law instead of trying to reconstruct their week on Friday afternoon.

We have product-market fit, and we're at the exciting moment where the constraint shifts from "build the thing" to "get it in front of every firm that needs it." If you want to join a YC company at the inflection point where its go-to-market engine kicks in, this is it.

Inlet builds automated timekeeping for law firms. We're YC S23, post-product-market-fit, and growing extremely fast with a strong mix of outbound and inbound.

We're hiring our first dedicated sales FTE - someone who wants to make a huge impact on top-of-funnel now and grow into a founding AE in 6-12 months.

• Execute our existing outbound playbook for small and mid-sized law firms.

• Book qualified demos - alleviating our current bottleneck and letting us scale even faster!

• You have 1-3 years of B2B outbound experience and have personally booked 10+ meetings/month with cold prospects.

• You're comfortable owning a quota and keeping track of your metrics.

• You move fast, communicate clearly in writing, and don't need to be asked twice.

• Legal tech experience or a background selling into law firms.

• Familiarity with Apollo, Exa, LinkedIn Sales Navigator.

• Founder mindset - you can run a sales function and continuously optimize it.

• Location: NYC or remote

• Compensation: $65-80K base + $20-30K variable (OTE $85K-$110K) + meaningful early-stage equity

• Reports to: Sean Adler (Founder/CEO)

• Start date: ASAP

We're an applied AI company. Much of what we do is building LLM pipelines that turn messy work artifacts - calendars, emails, documents, browser activity - into clean, defensible time entries that lawyers can actually put on an invoice.

Some of the interesting problems we're solving:

• Inference under ambiguity. In real-world environments, billable work can be difficult to classify. We have to reconstruct what an attorney did, for which client, for how long, from sparse and noisy signals.
• Trust and explainability. An attorney won't submit a time entry they can't defend to a client or a judge. Every suggestion we make has to be inspectable, editable, and traceable back to the underlying evidence.
• The long tail of legal practice. Billing conventions vary by practice area, firm, and even across individuals. We can't ship one rigid system - it has to adapt to how each firm actually works.

Stack: Python / React, Postgres, & GCP. We lean heavily on Claude Code and Codex in our engineering workflow, and we've built a lot of internal tooling to run our go-to-market.

We're a small team and the surface area is wide - engineers ship across the stack, talk directly to customers, and own problems end to end.