Machine Learning Engineer

Abin da za ka ɗauki alhakinsa

• 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.

Abin da za ka kawo

• 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.

Matakai biyar, kuma a kowanne ka san inda kake tsaye

1. 1Neman aikiKana nema ta wannan shafin. Mutum ne ke karanta shi — tacewa ta farko ba ta na’ura ba ce.
2. 2TattaunawaKira da manajan da ke ɗaukar ma’aikaci kan aikin kansa: abin da ka taɓa gudanarwa, da abin da gurbin aikin ke buƙata da gaske.
3. 3Gwajin aikiMatsalar da aka ciro daga tsarin gaske, cikin lokacin da ya dace. Ana tattaunawa a kanta tare da kai, ba a taɓa yi mata alama a asirce ba.
4. 4Kwamitin tambayoyiKana haɗuwa da mutanen da za ka yi aiki tare da su, da kuma wani daga wajen ƙungiyar aikin wanda ke nan domin yin tambaya mai wuya.
5. 5ShawaraEh ko a’a, tare da dalili. Muna gaya maka ko wanne ne, kuma muna gaya maka lokacin da za ka sa ran amsar.