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AI is at the center of Scribe. It is what allows us to build the OS our customers use to up level how work gets done. To enable that, ML engineers at Scribe bring unique strengths, yet there’s a shared ethos: high agency, purposeful curiosity, drive to build impactful solutions and grow together. If that resonates, you'll feel right at home!
💪 High agency — architecting the solution, not just the model
ML engineers at Scribe proactively shape their projects rather than simply executing tasks. We spot opportunities, set the north star, and drive projects from sketch to shipped. If a data gap appears, we patch it. If metrics are fuzzy, we re-define them. No script, no playbook—just the freedom (and accountability) to ship what matters.
🧠 Curiosity with purpose—turn research into reality
Paper skimming and one-off hacks don’t move the needle; disciplined exploration does. ML engineers at Scribe devour new papers, but take it further by stress-testing promising ideas under production constraints. We love to pull apart cutting-edge concepts, prototype fast, discard dead ends even faster, and iterate until an idea thrives in production. Weekly sessions keep failures visible, wins reproducible, and let us borrow each other’s breakthroughs while avoiding pitfalls.
⚡ Impact-driven simplicity—choose the tool that moves the needle
A novel attention mechanism isn't always the right answer; sometimes, a simple heuristic delivers far more user delight. At Scribe, we thoughtfully weigh GPU hours, latency budgets, and maintainability against tangible business outcomes, choosing the simplest approach that clearly translates into happier, more productive users.
Everything we ship is instrumented—from precise latency metrics to those rewarding "aha!" moments—so you can see the real-world impact of your work. When your solution noticeably improves user workflows, you'll hear the cheers (and candid feedback) firsthand!
🏋️♀️ Raising the bar together — candid reviews, shared wins
Every release is an opportunity to improve and refine. We probe data splits, question baselines, and demand statistical rigor—without ego. Successes (and bruises) belong to the team. Strong opinions are encouraged; defensiveness isn't.
Intellectual honesty is highly valued: openly discussing when results don’t replicate, revisiting assumptions, and documenting lessons learned so we avoid burning cycles on déjà-vu mistakes.
