Software Engineering Intern (Member of Technical Staff)
Job Description
Software Engineering Intern (Member of Technical Staff)
You'll work directly with the founders and build deep architecture of a shipped product with thousands of users that pushes multiple frontiers of tech and inference.
How we build
We code with agents: parallel Claude Code and Codex sessions across git worktrees, with per-feature docs written so humans and agents can both load context in minutes. You should naturally be intuitive with coding agents, and you should read and understand everything they produce. Our bar for quality and attention to detail is incredibly high.
Who we're looking for
- You understand systems deeply and love geeking out about interesting tech.
- You code with AI tools ∼daily. You have an intuition for their flaws and how to harness them well.
- We’d love if you've built and shipped something real that people actually love.
- You have an eye for great UX.
- You don’t mind spending 10x the time to perfect the last 10% (we pay you for all that 10x time :)
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Job Details
- Category
- Software
- Employment Type
- Internship
- Location
- US / Remote (US) (Remote)
- Posted
About Sentient OS
Nobody's built truly proactive AI, because it requires running inference on your entire life. In the cloud, that's insanely expensive and a privacy nightmare. But not on your own chip. Every night, Sentient's on-device LLM wakes your Mac and understands what's new in your life (email, messages, files, Granola transcripts...), entirely locally, and distills it into a knowledge base. You wake up to your work already prepared: the reply you forgot, drafted from your own context; the subscription you never use renewing tomorrow, caught; the report you promised in the group chat, ready to send. Everything is one click from firing through computer use, and nothing fires until you click. Or click your Mac notch anytime and say "finish this for me." Sidekick takes over the thing you were doing with its own cursor while you move on. On-device inference at this scale was supposed to be impossible: devices too slow, and models too dumb. So we built the stack ourselves: a custom LiteRT-LM fork (KV-cache reuse, speculative decoding, custom k-quants) that runs nightly on 8 GB Macs. The user's hardware does ~90% of the compute, and their own ChatGPT or Claude subscription does the rest, so our marginal cost is ~$0, and our servers store nothing. We soft-launched late July with one Reddit post: top of r/macapps, 2,000+ users in 48 hours, $0 marketing. Free and open source for consumers; the same engine on the work stack (Slack, Granola, Linear, Notion) becomes the enterprise business.
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