Machine Learning Engineer

Oyo okozala nkolo na yango

• 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.

Oyo omemi

• 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.

Matambe mitano, mpe na litambe moko na moko oyebi esika ozali

1. 1DemandeOsɛngaka mosala na nzela ya site oyo. Moto nde atangaka yango — filtrɛ ya liboso ezali ya masini te.
2. 2LisoloLisolo na telefone elongo na mokonzi oyo azali kozwa moto, likolo ya mosala yango moko: nini osili kotambwisa, mpe nini mosala esɛngi mpenza.
3. 3Ndakisa ya mosalaMokakatano moko oyo euti na sistɛm ya solo, oyo ekoki kosalema na ntango oyo ebongi. Esololamaka elongo na yo; bakatelaka yango likambo na nkuku ata mokolo moko te.
4. 4Bokutani na ekipeOkutani na bato oyo okosala na bango, mpe na moto moko ya libanda ya ekipe oyo azali wana mpo na kotuna motuna ya pasi.
5. 5MokanoEyano ya ɛɛ to ya tɛ, elongo na ntina na yango. Toyebisaka yo na ndenge nyonso, mpe toyebisaka yo mokolo nini okozela yango.