עדיין מחפשים עבודה במנועי חיפוש? הגיע הזמן להשתדרג!
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
מלאה
35,000-60,000 ₪
The role is simple to state and hard to do well: make that optimization save more tokens without ever costing output quality or breaking a provider's cache. It's a research-heavy engineering role - you form theories from real traffic and ship the ones that prove out on live customer requests.
Why this role
LLM optimization is a young field with no settled playbook, so the optimizations you invent here are genuinely new - and they ship into production, not a paper. And they matter: real enterprises run enormous volumes of LLM traffic and feel every wasted token, so the work you do lands on live customer requests and solves a problem they actually have. It's about as close to the frontier, and as close to the product, as engineering gets.
Why this role
LLM optimization is a young field with no settled playbook, so the optimizations you invent here are genuinely new - and they ship into production, not a paper. And they matter: real enterprises run enormous volumes of LLM traffic and feel every wasted token, so the work you do lands on live customer requests and solves a problem they actually have. It's about as close to the frontier, and as close to the product, as engineering gets.
Requirements:
Strong engineering fundamentals, and the judgment to tell when something is actually correct, fast and safe rather than just green in CI.
A real research instinct. You're fine with ambiguity, you form theories and drop them when the data says no, and you'd rather be right than clever.
You already build AI-native. Coding agents and LLM pipelines are part of your day, and you've used them to do work that used to take a team.
You know LLMs at a low level: tokenization, context windows, how caching actually behaves, streaming, tool calls, and how the various providers bill.
Staff-level range. You've carried big, vague pieces of work on your own and shaped how a team builds.
Strong engineering fundamentals, and the judgment to tell when something is actually correct, fast and safe rather than just green in CI.
A real research instinct. You're fine with ambiguity, you form theories and drop them when the data says no, and you'd rather be right than clever.
You already build AI-native. Coding agents and LLM pipelines are part of your day, and you've used them to do work that used to take a team.
You know LLMs at a low level: tokenization, context windows, how caching actually behaves, streaming, tool calls, and how the various providers bill.
Staff-level range. You've carried big, vague pieces of work on your own and shaped how a team builds.
This position is open to all candidates.
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
תל אביב - יפו
