Job description

WHO WE ARE 🏔Product: TLDR’s mission is to increase tech's signal-to-noise ratio. Today that means the largest network of tech newsletters in the world, with over 8M subscribers covering startups, software engineering, AI, cybersecurity, product, and more. What makes it work is who writes it. Every issue comes from people building in tech, not reporters covering it. Our writers keep their day jobs: two engineers at Coinbase write TLDR Crypto tldr.tech, engineers at DeepMind and Meta write TLDR Dev tldr.tech, researchers at Anthropic and Adobe write TLDR AI tldr.tech, and robotics and datacenter strategy leads at OpenAI and Meta write TLDR Hardware tldr.tech. If it matters in tech, it's in TLDR. That's what makes TLDR the best place to find what you need to learn. 💪Team: Our 31-person full-time team includes alumni of TikTok, Reddit, Amazon, Business Insider, Asana, Morning Brew, and Pinterest. We've stayed intentionally small, which means every person here owns a function rather than a slice of one. 📈Traction: We're bootstrapped, profitable, and on track for $35M in revenue this year - after $9M in 2024 and $20M in 2025. The advertisers who fund it want tech's decision-makers: AWS, Google Cloud, Anthropic, Slack, Notion, and GitHub. ABOUT THE ROLE TLDR's Applied AI program runs on one thesis: every process at the company should get better automatically as model capabilities improve. An LLM sits in the loop on every workflow, and the work of the people around it shifts from clicking and typing toward writing specs and evals and deciding what good looks like. In this role, you will: - Own the build out of new agents, skills, and platform capability for teams across TLDR. - Build and deploy agents end to end, from design through implementation, evals, and rollout to internal users. - Write the evals and specs that define what good looks like, so workflow quality is measurable rather than anecdotal. - Partner with our Applied AI PM on what to build, and make the smaller product calls independently. - Work directly with stakeholders in sales, revenue ops, editorial, and people ops to find where an LLM belongs in their process. WHAT SUCCESS LOOKS LIKE - Every workflow at the company improves as model capabilities improve. - Teams write specs and evals and define what good looks like, rather than executing manually, and their processes improve as models improve without anyone rebuilding them. - Work becomes increasingly push vs pull, by default work is done by LLMs and humans are brought in as needed. ABOUT YOU - 5+ years experience including 2+ years building products and systems with LLMs, with at least one agent you took to production and still own. - You code fluently with AI tools like Claude Code or Codex. - You write evals as part of building, not after. You can say how you defined good, what you measured, and what you changed as a result. YOU’LL THRIVE HERE IF… - You ship end to end and don't wait for someone to spec the small stuff. - You have product judgment: you can say what you chose not to build, and why. - You're comfortable in a domain where settled practice doesn't exist yet. - You can sit with non-engineering stakeholders and find where an LLM belongs in their process. - The way you build has changed as models have gotten better, and you treat capability improvement as a design input. - You want surface area across nearly every function of a profitable, bootstrapped company, with no legacy systems to fight, direct CEO access, and an unlimited token budget. WHY YOU'LL LOVE IT 🤑 Compensation: Total cash at target is $298,000 - $358,000 - Base salary: $240,000 to $300,000 - Annual bonus: $58,000 when we hit our company revenue goal, paid in cash each January, with upside up to $116,000 if we beat it 💲How we pay: One of our values is to simplify aggressively, and that includes pay. You get a base and a bonus, both in cash. We place you in the band based on what the interview process shows about your experience. If you’ve done this role at this level before, we’d typically start you around $328k in total cash, which leaves room to grow. You move up with performance, and we review pay every year. We keep the team small and pay well, so you work alongside strong people. If comp is the only thing stopping you from applying, tell us. 🌎 Location: We're 100% remote across the US and Canada. Work where you want - our bands are set to tier-1 city rates wherever you live. 🤝 Team Events: Biannual team offsites. Most recently we’ve gone to New Orleans, San Diego and Park City! 🏝️ Time to Recharge: Flexible PTO. Most of the team takes 2–3 weeks a year, plus holidays. 🏥 Health Benefits: Comprehensive medical, dental and vision benefits with a 100% paid option 📈 401(k) Plan: Empower 401(k) 🍼 Paid Parental Leave 💻 Home Office Stipend: Whatever makes you productive - standing desk, second monitor, chair, walking pad. 🔰 Learning & Development Stipend: For anything that makes you better at your work, and for AI tools especially. Don't ration your tokens. 📥 If you're ready to make a real dent at a bootstrapped, profitable company, apply. Tell us if you need any accommodation at any point in the process. PRESS AND PUBLICATIONS - Inc story on how TLDR was founded inc.com - Pricing and demographic information in TLDR’s latest media kit kit.tldr.tech

Requirements

Must-haves

Own the build out of new agents, skills, and platform capability for teams across TLDR.
Build and deploy agents end to end, from design through implementation, evals, and rollout to internal users.
Write the evals and specs that define what good looks like, so workflow quality is measurable rather than anecdotal.
Partner with our Applied AI PM on what to build, and make the smaller product calls independently.
Work directly with stakeholders in sales, revenue ops, editorial, and people ops to find where an LLM belongs in their process.
Every workflow at the company improves as model capabilities improve.
Teams write specs and evals and define what good looks like, rather than executing manually, and their processes improve as models improve without anyone rebuilding them.
Work becomes increasingly push vs pull, by default work is done by LLMs and humans are brought in as needed.
5+ years experience including 2+ years building products and systems with LLMs, with at least one agent you took to production and still own.
You code fluently with AI tools like Claude Code or Codex.
You write evals as part of building, not after. You can say how you defined good, what you measured, and what you changed as a result.
You ship end to end and don't wait for someone to spec the small stuff.
You have product judgment: you can say what you chose not to build, and why.
You're comfortable in a domain where settled practice doesn't exist yet.
You can sit with non-engineering stakeholders and find where an LLM belongs in their process.
The way you build has changed as models have gotten better, and you treat capability improvement as a design input.
You want surface area across nearly every function of a profitable, bootstrapped company, with no legacy systems to fight, direct CEO access, and an unlimited token budget.
Compensation: Total cash at target is $298,000 - $358,000
Base salary: $240,000 to $300,000
We keep the team small and pay well, so you work alongside strong people. If comp is the only thing stopping you from applying, tell us.
Location: We're 100% remote across the US and Canada. Work where you want - our bands are set to tier-1 city rates wherever you live.
Team Events: Biannual team offsites. Most recently we’ve gone to New Orleans, San Diego and Park City!

Nice-to-haves

Annual bonus: $58,000 when we hit our company revenue goal, paid in cash each January, with upside up to $116,000 if we beat it
How we pay: One of our values is to simplify aggressively, and that includes pay. You get a base and a bonus, both in cash.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiawsllm

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Parental Leave