Machine Learning Engineer

ኃላፊነቶች

• 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.

መስፈርቶች

• 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 የሥራ መደብ ላይ የሚተገበሩ

1. 1ማመልከቻማመልከቻው በዚህ ድረ-ገጽ በኩል ይቀርባል፤ በሰው ኃይል አስተዳደርም ለሥራ መደቡ በታተሙት መስፈርቶች መሠረት ይነበባል።
2. 2ውይይትአመልካቹ ስለገነባቸው ወይም ስላንቀሳቀሳቸው ሥርዓቶች ከቀጣሪው ኃላፊ ጋር የሚደረግ ጥሪ።
3. 3የሥራ ናሙናግሩፑ ከሚያንቀሳቅሰው ሥርዓት የተወሰደ ችግር፤ በአንድ ክፍለ ጊዜ የሚያልቅና ካዘጋጁት መሐንዲሶች ጋር የሚወያዩበት።
4. 4የቃለ መጠይቅ ቡድንየሥራ መደቡ ካለበት ቡድንና ከግሩፑ ሌላ ክፍል ከመጣ ገምጋሚ ጋር የሚደረግ ፓናል፤ ቴክኒካዊ ጥልቀትንና የአሠራር ልምድን ይዳስሳል።
5. 5ውሳኔቀጣሪው ኃላፊና የሰው ኃይል አስተዳደር በጋራ ይወስናሉ፤ ውጤቱንም ከምክንያቱ ጋር ወደዚህ ደረጃ ለደረሰ እያንዳንዱ አመልካች ያሳውቃሉ።