Machine Learning Engineer

የሚረከቡት ኃላፊነት

• Take speech and language models from evaluation into production, including the serving path and what it costs to run.
• Build evaluation sets that reflect the languages and accents customers actually speak, not the ones a public benchmark ships with.
• Instrument models in production: drift, latency, and the failure cases customers report.
• Work with the platform team on the data path, so that training data stays in the jurisdiction the customer requires.
• Document model behaviour and its limits to a standard a regulator can read.

ይዘው የሚመጡት ብቃት

• You have deployed models to production and maintained them afterwards, not only trained them.
• You are comfortable with PyTorch or an equivalent framework, and with the serving stack around it.
• You understand speech or natural language processing well enough to know where the benchmarks mislead.
• You treat privacy and data residency as engineering constraints rather than as paperwork.
• You work in English. Other languages spoken in the group’s markets are an advantage.

አምስት ደረጃዎች፤ በእያንዳንዱ ላይ የት እንደደረሱ ያውቃሉ

1. 1ማመልከቻበዚህ ድረ-ገጽ በኩል ያመለክታሉ። ማመልከቻዎን የሚያነበው ሰው ነው — የመጀመሪያው ማጣሪያ በማሽን አይሠራም።
2. 2ውይይትስለ ሥራው ራሱ ከቀጣሪው ኃላፊ ጋር የሚደረግ ጥሪ፦ እስካሁን ምን አንቀሳቅሰው እንደሚያውቁና የሥራ መደቡ በእውነት ምን እንደሚፈልግ።
3. 3የሥራ ናሙናከእውነተኛው ስርዓት የተወሰደ ችግር ነው፤ በአንድ ምክንያታዊ ክፍለ ጊዜ የሚያልቅ። ውጤቱ ከእርስዎ ጋር ይወያያል እንጂ በዝምታ አይመዘንም።
4. 4የቃለ መጠይቅ ቡድንአብረዋቸው የሚሠሩትን ሰዎች ያገኛሉ፤ እንዲሁም ከቡድኑ ውጭ የሆነና አስቸጋሪውን ጥያቄ ለመጠየቅ የሚገኝ አንድ ሰው።
5. 5ውሳኔአዎ ወይም አይሆንም፤ ከምክንያቱ ጋር። ውጤቱ ምንም ይሁን ምን እንነግርዎታለን፤ መቼ እንደሚደርስዎም አስቀድመን እንገልጻለን።