Senior Software Engineer, Applied AI
Builds and deploys LLM agents end to end for internal teams across TLDR — design, implementation, evals, and rollout to internal users — partnering with stakeholders in sales, revenue ops, editorial, and people ops. Core stack: LLM agents, evals/specs, and AI coding tools like Claude Code and Codex.
- Compensation
- $298K–$358K / yr
- Location
- Remote
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Full time
- Experience
- 2+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 5+ years experience, including 2+ years building products and systems with LLMs
- At least one agent taken to production and still owned
- Codes fluently with AI tools like Claude Code or Codex
- Writes evals as part of building, with clear definition of what was measured and changed
Nice to have
- Ships end to end without waiting for someone to spec the small stuff
- Product judgment: can articulate what was chosen not to build and why
- Comfortable in a domain where settled practice doesn't exist yet
- Comfortable working with non-engineering stakeholders to find where an LLM belongs in their process
Full 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, engineers at DeepMind and Meta write TLDR Dev, researchers at Anthropic and Adobe write TLDR AI, and robotics and datacenter strategy leads at OpenAI and Meta write TLDR Hardware.
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.