Machine Learning Engineer

Responsibilities

• 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.

Requirements

• 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.

Five steps, applied to every role

1. 1ApplicationSubmitted through this site and read by Human Resources against the requirements published for the role.
2. 2ConversationA call with the hiring manager about the systems the candidate has built or operated.
3. 3Work sampleA problem from a live group system, scoped to one session and discussed with the engineers who set it.
4. 4PanelThe hiring team and a reviewer from elsewhere in the group, on technical depth and operating practice.
5. 5DecisionThe hiring manager and Human Resources decide together. Every candidate at this stage is told the outcome and why.