Machine Learning Engineer

Kile utakachosimamia

• 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.

Kile unacholeta

• 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.

Hatua tano, na katika kila moja unajua umefikia wapi

1. 1MaombiUnatuma maombi kupitia tovuti hii. Mtu huyasoma — kichujio cha kwanza si cha kiotomatiki.
2. 2MazungumzoSimu na meneja anayeajiri kuhusu kazi yenyewe: ulichokwisha kukiendesha, na kile nafasi inachohitaji hasa.
3. 3Kazi ya mfanoTatizo lililotolewa katika mfumo halisi, lililopangwa kukamilika ndani ya kikao kimoja cha muda unaofaa. Hujadiliwa nawe, halipimwi kimyakimya.
4. 4PaneliUnakutana na watu ambao ungefanya nao kazi, na mtu kutoka nje ya timu aliyepo ili kuuliza swali gumu.
5. 5UamuziNdiyo au hapana, pamoja na sababu. Tunakujulisha vyovyote itakavyokuwa, na tunakwambia lini uutarajie.